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With the advance of the Silk Road Initiative proposed by China, it has been a focus of China government to develop strategic emerging industries. The development of strategic emerging industries needs the support of competitive intelligence on many aspects such as strategical planning, policy making, industrial structure adjustment, and technology innovation. However, so far there are few studies toward the competitive intelligence systems for strategic emerging industries. In this article, we focus on a number of issues related to the competitive intelligence for strategic emerging industries in China. First, we conduct a strengths, weaknesses, opportunities, and threats analysis on the situations of strategic emerging industries in China, based on which the necessity of building a competitive intelligence (CI) service system for strategic emerging industries is discussed. Next, the authors present a framework of a CI service system for strategic emerging industries in China. The principles, components, working process, and product forms are deeply described. The CI service system proposed in this article consists of a cooperation network platform, three layered organizations, and three systems, which integrates organizations, information, people, network, and service platforms into an ecosystem to offer competitive intelligence supports for government, industry, and enterprises. Finally, the authors discuss a case study of the proposed CI service system for the new energy automobile industry.

In as much as it is contested, the Belt and Road Initiative (BRI) is also unexplored, underdiscussed, and, as a result, misunderstood. Frequently viewed through the lens of international relations and global economy, the diverse dimensions of collaboration, including business and research-industry clusters, that BRI enhances, tend to be excluded from the analysis. In a similar manner, the role of the Arab Peninsula in the grand strategy underpinning BRI and its implementation is rarely discussed. BRI is a forward-oriented initiative, an attempt to reap benefits of developments and circumstances that are only nascent. This bears two potent implications. First, as China attempts to influence the context in which it operates, it is subject to change itself; the Chinese business sector evolution attests to that. Second, some of China’s not so obvious partners of today, including those in the Arab Peninsula, are about to turn into key interlocutors of tomorrow. BRI taps into opportunities thus created. This chapter elaborates on these issues and, against this backdrop, outlines how the remaining chapters included in this volume add to this discussion.

This paper examines the problem of lack of historical data and inadequate consideration of factors influencing demand in the forecasting of demand for fast fashion clothing and proposes an improved Bass model for the forecasting of such a demand and the demand for new clothing products.

From the perspective of how to solve the lack of data and improve the precision of the clothing demand forecast, this paper studies the measurement of clothing similarity and the addition of demand impact factors. Using the fuzzy clustering–rough set method, the degree of resemblance of clothing is determined, which provides a basis for the scientific utilisation of historical data of similar clothing to forecast the demand for new clothing. Besides, combining the influence of consumer preferences and seasonality on demand forecasting, an improved Bass model for a fast fashion clothing demand forecast is proposed. Finally, with a forecasting example of demand for clothing, this study also tests the validity of the method.

The objective measurement method of clothing similarity in this paper solves the problem of the difficult forecasting of demand for fast fashion clothing due to a lack of sales data at the preliminary stage of the clothing launch. The improved Bass model combines, comprehensively, consumer preferences and seasonality and enhances the forecast precision of demand for fast fashion clothing.

The paper puts forward a scientific, quantitative method for the forecasting of new clothing products using historical sales data of similar clothing, thus solving the problem of lack of sales data of the fashion.

During the industrial design process, a product is usually modified and analyzed repeatedly until reaching the final design. Modifying the model and regenerating a mesh for every update during this process is very time consuming. To improve efficiency, it is necessary to circumvent the computer-aided design modeling stage when possible and directly modify the meshes to save valuable time. The purpose of this paper is to develop a method for mesh modifications.

In contrast to existing studies, which focus on one or a class of modifications, this paper comprehensively studies mesh union, mesh gluing, mesh cutting and mesh partitioning. To improve the efficiency of the method, the paper presents a fast and effective surface mesh remeshing algorithm based on a ball-packing method and controls the remeshing regions with a size field.

Examples and results show that the proposed mesh modification method is efficient and effective. The proposed method can be also applied to meshes with different material properties, which is very different with previous work that is only suitable for the meshes with same material property.

This paper proposes an efficient and comprehensive tetrahedral mesh modification method, through which engineers can directly modify meshes instead of models and save time.

A green innovation strategy is an important step for enterprises to balance economic and environmental. As the executors of strategic decisions, the attitude and capabilities of senior managers determine the effectiveness of implementing green innovation. Therefore, this paper aims to explore the relationship between executive compensation incentives and green innovation.

Based on the data of heavily polluting enterprises listed in China's A-share market from 2015 to 2020, this study constructs an OLS model with fixed effects of time and industry, and uses the mediation three-step method to verify the correlation between executive compensation incentives, innovation openness and green innovation. Meanwhile, the grouping regression was used to test the moderating effect of environmental regulation on executive compensation incentives.

The empirical results show that executive salary incentives promote green innovation and equity incentives inhibit green innovation; the openness breadth partially mediates the relationship between salary incentives, equity incentives and green innovation, while the openness depth only partially mediates the relationship between equity incentives and green innovation; and environmental regulation positively moderates executive incentives.

Due to sample selection and variable measurement, the study lacks certain generality. Therefore, future research needs to further analyze the internal factors affecting green innovation from multiple dimensions.

This study provides a new evidence for analyzing how executive compensation measures affect green innovation, and further enhances the mediating mechanism of open innovation.

This study has significant theoretical implications for examining the intra-firm factors that affect green innovation.

The advent of the new retail era witnessed the consumers’ demand shift from on the traditional product quality to on the full supply chain service quality, and product service and service manufacturing is gradually taking shape. The purpose of this paper is to propose whether there is a “quality bridge” in the dynamic evolution process of retail service supply chain (RSSC) and discuss the system role, steady-state characteristics and dynamic evolution mechanism of service quality in this dynamic evolution process.

This paper proposes the dissipation system structure of the RSSC under the steady-state quality constraint, constructs a Markov chain model (MCM) for the evolution of the service quality of RSSC, and tests the objective existence of the steady-state distribution of the service quality by taking Chinese HJ retail enterprises as samples.

The research value of this study is summarized as follows. The research finds that the evolution of service quality of RSSC is a dynamic and non-linear growth process, which has significant characteristics of complex adaptability and steady-state convergence. The study finds that the quality evolution process of the RSSC is a steady-state convergence process, and there is a steady-state distribution of quality in its co-evolution, in which different process input levels have a significant positive impact on the stable level of quality state. The study finds that the steady state of quality plays a crucial role in the collaborative evolution of the RSSC, that is, when the service quality reaches a certain steady state distribution, the operating efficiency and profit level of the whole chain will show an “explosive” growth trend.

Quality bridge, an original concept in this paper, represents the role of quality steady-state in the operation of RSSC. Based on Markov chain and system simulation tools, this paper verifies the existence of steady-state service quality and its positive effect on the co-evolution and sustainable development of RSSC. When the service quality reaches a certain steady distribution, the operating efficiency and income level of the whole chain will show n trend of explosive growth.

Based on the analysis of the dissipative structure of the retail service supply chain (RSSC), this paper divides the system into two internal and external dissipative mechanisms, including the internal performance dissipation mechanism and the perceived quality dissipation mechanism outside the system. Based on the prediction of RSSC performance, this paper aims to discuss the application of Hidden Markov Model (HMM) in this field and puts forward a set of complete process of forecasting the service supply chain (SSC) performance based on HMM model.

Based on the theory of dissipative structure, this paper selects the RSSC as the research object, analyzes the system characteristics of the dissipation structure of RSSC from three aspects, such as system opening type, distance from equilibrium state and nonlinear order and describes the quality fluctuation process of RSSC as a Hidden Markov process. Taking the RSSC of J Company as an example, this paper makes use of the observed state value of customer perceived service quality from 1997 to 2016, predicts the performance status of the enterprise's RSSC.

The research results show that: RSSC is a dissipative structure system, and its performance is the internal entropy flow of the system, and the customer perceived service quality is external, their interaction determines the dynamic evolution of the system dissipation structure, and the Markov property between supply chain performance and perceived service quality. There is a Markov property between supply chain performance and perceived service quality. Using the perceived service quality observation state data of the external consumers of the system can effectively predict the implicit state of RSSC performance. Based on this prediction result, the strategy adjustment and optimization of the action mechanism of internal and external entropy flow in the dissipative structure system can be carried out to promote the sustainable development of the RSSC.

This paper thinks that RSSC is a dissipative structure system and the SSC performance and customer perceived service quality are the internal and external entropy flow of the system, which determines the dynamic evolution of the system dissipation structure. There is a Markov property between supply chain performance and perceived service quality. The hidden state of SSC performance can be predicted effectively by using a hidden Markov model and observing state data of perceived service quality from consumers outside the system.

The purpose of this paper is to study corrosion behavior and corrosion mechanism of carbon steel in natural freshwaters with humic acid (HA).

The weight‐loss method, electrochemical measurements and surface analysis technique were used to study the corrosion behavior of carbon steel in freshwaters with HA.

General corrosion occurred when carbon steel was exposed to test solutions (fresh lake water with and without HA). The presence of HA in test solution can accelerated the corrosion rate of carbon steel.

There have been few reports on the effect of HA on the corrosion behavior of carbon steel. In this study, it is clearly shown that the presence of HA in fresh lake water can accelerate the corrosion rate of carbon steel. This research not only can offer theoretical guidelines for the prevention of pipelines and facilities from corroding, but also can provide a reference for suitable material selection and the further protection of carbon steel in hydraulic engineering.

The purpose of this paper is to properly calibrate the laser tracking system (LTS) prior to using it for metrology and improving the measuring accuracy of LTS.

A kinematics model that describes not only the motion but also geometric variations of LTS is developed. Effects of tracking mirror mechanism dimension errors on measured coordinates and target reflector alignment errors on sensor reading are investigated.

Through error analysis of the proposed model, it is claimed that gimbals axis misalignments and tracking mirror center offset are the key contributors to measure errors of LTS. Intensive simulation studies are conducted to check the validity of the theoretical results and various practical issues are also explored in the simulations. The simulation results demonstrate that under realistic conditions the 10‐parameter model is the minimal and complete model.

This model, together with its error model which is also presented in this paper, can be used for design, calibration, and control of LTS.

This paper develops a kinematics model that describes not only the motion but also geometric variations of LTS, and demonstrates that gimbals axis misalignments and tracking mirror center offset is the key contributor to measuring errors of LTS.