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Introduction of value chain management thinking,systematically analyzed the internal operating costs and the external competitiveness costs of construction enterprises. Derived driving factors of the operating costs using internal value chain,and analyzed the competitive cost using the industry value chain.In the CCSES case study, proposed a few methods to control cost drivers and improvement strategies using the above analysis.

From the perspective of commercial banks, put the financial supply chain in the principal-agent relationship which is multi-agent and of multiple sections. This paper analyzed the principal-agent relationship between different subjects and commercial banks and information asymmetry. Secondly it also analyzes the information risk of supply chain finance caused by asymmetric information from in three aspects as risk types, risk behavior and risk loss. Finally it gives the Suggestions and countermeasures aimed to financial risk in supply chain information. It provides reference to main body understanding and the information risk of supply chain finance.

The core competitive index system is proposed from construction enterprises characteristics. Due to fuzzy characteristics of core competitive index, a multi-level gray evaluation model is established for construction enterprises. And the effectiveness of this model is illustrated by an example.

The innovative value chain system and the operation mechanism were studied of the service enhanced construction. In the service enhanced construction era, value chain innovation system includes orientation system integration services, full life cycle project management and engineering general contracting system. The service enhanced construction needs perfect construction market environment, the trust and sharing information in business, coordinated management and knowledge management, to reconstruct the value chain system, to achieve the structural adjustment of the construction industry and to improve the core competitiveness of enterprises.

The volatile and semi-volatile components in tobacco flavor additives were extracted by both simultaneous distillation extraction and solid-phase micro-extraction. Extraction conditions for solid-phase micro-extraction were optimized with information theory. Then, detection were accomplished by gas chromatography-mass spectrometry. Characteristic of each method was compared. Qualitative analysis and quantitative analysis of 6# tobacco flavor sample were accomplished through both simultaneous distillation extraction and solid-phase micro-extraction. The experimental results show that solid-phase micro-extraction method is the first choice for qualitative analysis and simultaneous distillation extraction is another good selection for quantitative analysis. By means of simultaneous distillation extraction, 20 components are identified, accounting for 92.77% of the total peak areas. Through solidphase micro-extraction, there are 17 components identified accounting for 91.49% of the total peak areas. The main aromatic components in 6# tobacco flavor sample are propanoic acid, 2-hydroxy-, ethyl ester, menthol and menthyl acetate. The presented method has been successfully used for quality control of tobacco flavor.

The probability reasoning method, fuzzy reasoning method, evidential reasoning method and other reasoning methods are main techniques employed in intelligent systems for processing uncertain and vague information. The concept of inclusion degree was proposed earlier and it has been proved that the methods mentioned above are examples of inclusion degrees. In this paper, we introduce type S1 and type S2 inclusion degrees, discuss the relationship between them, and further propose inclusion degrees on interval numbers, divisions, vectors and set vectors. This paper addresses an uncertainty analysis method with different inclusion degrees for intelligent systems and other systems such as fuzzy relational databases.

An improved ethylene blue method for determination of sulfide is developed. It has been adapted to a direct determination of sulfide by both common spectrophotometric method and total differential spectrophotometric method. In common spectrophotometric method, the calibration curve is A=1.69ρ+0.006 and the correlation coefficient is 0.9994. The apparent molar absorptivity is 5.42 × 104 L·mol−1·cm−1 and calibration curve is liner when ρ is in the range of 0–0.9 mg·L−1. In total differential spectrophotometric method, the calibration curve is A=9.25ρ + 0.004 and the correlation coefficient is 0.9996. The apparent molar absorptivity is 2.96 × 105 L·mol−1 L·mol−1·cm−1 and calibration curve is liner when ρ is in the range of 0–0.10 mg·L−1. The sensitivity of this method is increased significantly compared with the former ethylene blue method. The speed of reaction is also faster than the former one. The limit of detection is found to be 1.0 ng·mL−1 by both common spectrophotometric method and total differential spectrophotometric method. Ten replicate analyses of a sample solution containing 100 ng·mL−1 sulfide give a relative standard deviation of 1.8%. The effects of various cations and anions on the determination of sulfide are studied and procedures for removal of interference is described. The method is used for the determination of sulfide in environment samples with satisfactory results.

Four kinds of basic decision rule sets are respectively produced from the formal concept lattice, the dual formal concept lattice, the object-oriented concept lattice and the attribute-oriented concept lattice in a formal context. Based on these sets, two types of decision inferences are established via an inclusion degree. The corresponding decisions by inferences are proved to be the lower and the upper approximated decisions. Thus total decision rules described as the lower and the upper approximated decision rules are obtained. And they are accordant and consistent with the basic decision rule sets.

From the classic view in which a concept consists of the set of extents and the set of intents, a concept learning system extended from a formal context is introduced and two concepts such as an under concept and an over concept are defined. Any pair of subsets from extents and intents in this concept learning system can be changed to an under or an over concept. Further it can be changed to a concept by learning from the set of extents or from the set of intents. It is proved that the concept learned in this framework is an optimal concept. This process of learning a concept describes the recognizing ability from unclear to clear.

We discuss characterizations of three important types of attribute sets in generalized approximation representation spaces, in which binary relations on the universe are reflexive. Many information tables, such as consistent or inconsistent decision tables, variable precision rough set models, consistent decision tables with ordered valued domains and with continuous valued domains, and decision tables with fuzzy decisions, can be unified to generalized approximation representation spaces. A general approach to knowledge reduction based on rough set theory is proposed.