In recent years, China's outward foreign direct investment (OFDI) has risen significantly, arousing considerable interest in the motivations and drivers of Chinese overseas investment. This paper selected 27 host country-related indicators and extracted the common factors using the factor analysis method. This paper discusses the determinants of China's OFDI location choice by using panel data regression method, and focuses on the differences between Belt and Road countries and nonBelt and Road countries. The results show that the favorable institutional environment and strong market demand of host countries have a positive influence on Chinese foreign investment. Besides, China's investment in Belt and Road Initiative countries is more prone to a country with less developed technology and unreasonable energy utilization. China's OFDI can promote technological progress in these countries and making full use of their advantage resources for economic development. As China's economy has entered a "new normal", its global influence has risen, and the relationship between the host countries and China has also mattered more on China's OFDI decisions. This effort provides important supports for optimizing the location choices of Chinese enterprises' foreign investment.

The paper first analyzes price change due to stock splits in Chinese stock markets, which shows stock prices typically go up for stock splits. Then theoretical analyses based on risk theory are presented to explain the reason, where the method comes from a new perspective and obtained theoretical conclusions show that stock splits typically make stock price go up if risk-compensation function is convex, and go down if risk-compensation function is concave. Stock prices typically go up for stock splits because risk-compensation functions are mainly convex. The obtained conclusions are consistent with the known results in the last three decades.

With the development of urbanization in China, tons of municipal solid waste have been produced and disposed Incineration is the best way to deal with municipal solid waste in China but this practice often is opposed and resisted by the public who live nearby. This study systematically analyzed the risk responses of the public, in particular factors affecting the public's resistant behavior We conducted a survey and collected 376 valid questionnaires which we used for the analysis. We used the structural equation model and path analysis for the examination, and the results showed that risk perception was a critical factor predicting the resistant behavior of the public surrounding the wasteto-energy (WTE) plant. Benefit perception had a negative, but insignificant, impact on the public's resistant behavior. We found a negative correlation between benefit perception and risk perception but the relationship was weakened when we added systematic processing to the path analysis. The impact of system processing on risk perception was greater than that of benefit perception; that is, systematic processing was better in explaining the risk judgment of the public than benefit perception. Problem knowledge was a significant indicator in predicting risk perception and systematic processing and technology knowledge was a significant indicator in predicting risk perception and benefit perception. Systematic processing increased the public's risk judgment to the WTE plant. Finally, we discussed practical implications and limitations.

Currently, renewable energy generation has received more and more attention. This article focuses on wind energy generation, one of the renewable energy sources. Aiming at the intermittent and unpredictable wind power problems, according to the day ahead bidding mechanism in the power market, this paper introduces the energy storage system to maximize wind power merchants profit based on the newsvendor model. First, this paper focuses on the wind farms combined with storage system to put forward the optimal bidding decision of selling or buying electricity to the market one day in advance and the optimal bidding amount. Then, we analyze the relationship between the optimal bid amount and the market penalty price coefficient, government subsidy, storage system charge and discharge efficiency, and other parameters. Based on the analysis of various parameters, power generation companies can make reasonable bidding decisions and amount to maximize profits.

With the rapid development of mobile communication equipment, the significant role of social media platforms is realized in social media marketing. To determine the effect of instant messaging social media platform characteristics on consumers' purchase intention, we collected WeChat user data and designed an empirical model based on the technology acceptance theory. Analysis of 388 qualified surveys revealed significant positive effects of instant messaging social media platform characteristics, such as social presence, media richness, immediacy of communication, privacy protection, and entertainment on customers' purchase intention. This study aims to extend the scope of technology acceptance theory, providing practical ideas for firms and highlighting the prominent role of instant messaging social media platforms in marketing activities.

Since firms are the entities of technological innovation and basic research is the source of technological innovation, encouraging firms to conduct basic research has gained increasing attention. Using Huawei Technologies as a case study, this study employs a vector autoregressive (VAR) model and relevant data of the target firm from 2000 to 2019 to investigate the relationships between basic research, technological innovation, and firm performance. According to the findings, basic research has a positive effect on technological innovation but has a significant lag; technological innovation has a positive effect on firm performance and shows significant periodicity; while basic research has a positive effect on firm performance and a procedural nature. On this basis, we put forward some enlightening suggestions for firms to carry out basic research.

This study selected 1558 literatures on tourism and environmental management from 1981 to 2020, and systematically analyzed the research hotspots and evolution according to the co-citation network and keyword analysis. We find that these documents focus on the sustainable management patterns in marine and coastal tourism, the environmental management practice of tourism and hotel industry, and the attitude of tourism stakeholders towards environmental management policies. We summarize the status quo, implications and suggestions. In the future, waste management in tourism areas and the competitive advantages established through environmental management would become the new research hotspots.

This paper studies two manufacturer rebate strategies in a green supply chain. The results show that: 1) Irrespective of the type of rebate strategy used, channel members benefit; 2) Rebates for green products improve the green level of green products; 3) The more significant the slippage effects are, the more beneficial the rebate strategies are, for channel members. On this basis, the model is expanded to consider asymmetric potential market demands and asymmetric product substitution rates, respectively. The results show that the product substitution rate does not affect the strategic preference of channel members, but the potential market demand does.

Many studies have shown that energy consumption plays an important role in economic growth. The paper researches the influence of energy consumption on economic growth in China's Yangtze River Economic Zone. The paper divides the energy of Yangtze River Economic Zone into the coal, the oil, the natural gas and the electricity and explores the influences of coal consumption, gas consumption, natural gas consumption and electricity consumption on economic growth quantitatively using an extended production function model. The paper mainly uses two methods. The first method is the output elasticity analysis. The paper calculates the four energy consumption's output elasticity to economic growth to compares the influences of energy consumption in terms of out output elasticity. The second method is the contribution rate analysis. The paper calculates the contribution rates of four energy consumption to economic growth to compare the influences of four energy consumption on economic growth in terms of contribution rate. The paper makes an empirical analysis on the influence of energy consumption on economic growth in China's Yangtze River Economic Zone. Analysis results show that oil consumption has the greatest influence on economic growth in China's Yangtze River Economic Zone, in terms of both output elasticity and contribution rate, followed by natural gas consumption, electricity consumption and coal consumption.

Accurately forecasting short-term electricity prices is of great significance to electricity market participants. Compared with the time series forecasting methods, machine learning forecasting methods can consider more external factors. The forecasting accuracy of machine learning models is greatly affected by the parameters, meanwhile, the manual selection of parameters usually cannot guarantee the accuracy and stability of the forecasting. Therefore, this paper proposes a random forest (RF) electricity price forecasting model based on the grey wolf optimizer (GWO) to improve the accuracy of forecasting. Among them, RF has a good ability to deal with the problem of non-linear and unstable electricity prices. The optimization of model parameters by GWO can overcome the instability of the forecasting accuracy of manually tune parameters. On this basis, the short-term electricity prices of the PJM power market in four seasons are separately predicted. Experimental results show that the RF algorithm can better predict the short-term electricity price, and the optimization of the RF forecasting model by GWO can effectively improve the accuracy of the RF forecasting model.

The development and utilization of new and renewable resources of energy has become an important layout of the development strategy in China. Photovoltaic industry is an important strategic emerging industry for the development and utilization of new energy in China. Therefore, it is important for the government to make policy to ensure the stable and orderly development of photovoltaic enterprises to accelerate the industrial structure transition in China. This paper collects the policies on photovoltaic industry, and then analyzes the industrial policy with Latent Dirichlet Allocation (LDA). LDA is generally used in document topic label extraction and recommendation system. However, this paper applies it to policy theme analysis to study the impact of policy information flow on the risk of photovoltaic enterprises. Previous studies on photovoltaic enterprise risk examined traditional financial indicators, such as asset-liability Ratio and ROE. However, the textual information in the industrial policy has rarely been studied to quantitatively analyze photovoltaic enterprise risk. In our proposed method, LDA is first used to extract the text features hiding in the text of the industrial policies, and deep neural networks then are trained on the data, which include the text features and traditional numeric features for predict photovoltaic enterprise risk. The experimental results show that the industrial policy of the current quarter has a significant effect on photovoltaic enterprise risk. Compared with this, the industrial policy of last quarter has a weak impact on the photovoltaic enterprise risk. The proposed model is a useful tool for the prediction of the photovoltaic enterprise risk.

In our daily life, it is nothing strange to see pixelated images that are spoiled artificially to hide certain information for protecting privacy or pixelated deliberately to cover up bad behaviors even crimes. To prevent these phenomena and recover the true information from pixelated images, it is meaningful to research an effective reconstruction method for recovering pixelated images. This paper aims at recovering the artificial partial pixelated images via deep learning (DL). To abstract more abundant features and enhance the repair ability of DL model, we propose a new DL structure, called deeper inception U-Net, to act as the generator of a generative adversarial network. We combine the feature loss with structural similarity index measure loss as the context loss to minimize the distance between feature maps of clear images and the generated images, which helps to improve the quality of repair images. After obtaining inception features, we use fusion layer to adaptively learn features in each inception block. To evaluate the performance of our model, we introduce a new home dataset that contains 10174 clear home images with corresponding pixelated images. A series of experiments show that our model has ability to rebuild pixelated images.

The goal of sentiment analysis is to detect the opinion polarities of people towards specific targets. For fine-grained analysis, aspect-based sentiment analysis (ABSA) is a challenging subtask of sentiment analysis. The goals of most literature are to judge sentiment orientation for a single aspect, but the entities aspects belong to are ignored. Sequence-based methods, such as LSTM, or tagging schemas, such as BIO, always rely on relative distances to target words or accurate positions of targets in sentences. It will require more detailed annotations if the target words do not appear in sentences. In this paper, we discuss a scenario where there are multiple entities and shared aspects in multiple sentences. The task is to predict the sentiment polarities of different pairs, i.e., (entity, aspect) in each sample, and the target entities or aspects are not guaranteed to exist in texts. After converting the long sequences to dependency relation-connected graphs, the dependency distances are embedded automatically to generate contextual representations during iterations. We adopt partly densely connected graph convolutional networks with multi-head attention mechanisms to judge the sentiment polarities for pairs of entities and aspects. The experiments conducted on a Chinese dataset demonstrate the effectiveness of the method. We also explore the influences of different attention mechanisms and the connection manners of sentences on the tasks.

The investment in green technology in the process of product design and production is viewed as a powerful tool for sustainable development and carbon emission reduction. However, the substantial cost and pressure of competition weaken incentives for manufacturers to engage in green technology. In this paper, we consider two competitive manufacturer-retailer supply chains, where each manufacturer sells partially substitutable products through the exclusive retailer, study green technology investment selection by manufacturers, and examine the efficacy of retailer cost sharing scheme. Our analysis shows that a dominant equilibrium strategy for both manufacturers is to invest in green technologies, whether cost sharing is in place or not. Retailer sharing the cost of manufacturer green technology investment can avoid firms' preference confliction over the green technology investment and improve social welfare simultaneously when both the cost-sharing rate and the degree of product/channel competition are relatively low. We also find that green technology investment by manufacturers does not necessarily curb total carbon emission, and the cost sharing can either strengthen or weaken the carbon emission reduction of green technology investment.

Accurate knowledge of who knows whom in organizations have important benefits for individual work performance and managerial decision making, but people are not very accurate when recalling connections among others in their social networks. The present study investigates how holisticanalytic thinking styles influence the extent people can accurately perceive network relationships and choose the right persons to form a coalition in a fictious persuasive task. We focused on two dimensions of holistic-analytic thinking style, namely, attention to field (as opposed to parts) and interactionist (as opposed to dispositionist) causal theory. Results from 281 participants reveals that while individuals with greater attention to field were more accurate in recalling relationships in a social network, those inclined toward interactionism in causal theory were less accurate. Furthermore, greater attention to field enhanced the effectiveness of coalition member selection, in part through the mediation of accurate network perception; while interactionism, via the full mediation of network perception, indirectly led to less effective coalition choice.

The MGM(1, m, N) model is an effective grey multi-variate forecasting model that considers multiple system characteristic sequences affected by multiple factors. Nevertheless, it is regularly inaccurate in the application. This is because the model requires a strong correlation between the system characteristic sequences. That reduces the applicability of the model. To solve this problem, this paper proposes a novel multi-variate grey model. This model does not require a certain correlation between system characteristic sequences and has higher applicability. Through numerical integration, a two-point trapezoidal formula, and a recursive method, the time-response expressions of the two model forms are obtained. Some properties of the proposed model are further discussed. Finally, the validity of the proposed model is evaluated by using two real cases related to China's invention patent development. The results show that the novel models outperform other models in both simulation and prediction applications.

Aiming at the dynamic multi-attribute decision making problem where the weight of each decision stage and attribute weight are completely unknown and the attribute value is unknown distributed three-parameter interval grey number, a three-parameter interval grey number dynamic multiattribute grey target decision making method with attribute value following quasi-normal distribution is proposed. Firstly, the position relationship between the "center of gravity" point and the kernel of the three-parameter interval grey number is discussed. According to the characteristic that the attribute value obeys the quasi-normal distribution, a new weight is given to the "center of gravity" point, and a new distance measure formula of the three-parameter interval grey number is defined. Secondly, according to the principle of maximum entropy, the objective programming model is constructed to determine the stage weight and attribute weight. Then, the schemes are sorted according to the size of the comprehensive bull's-eye-distance. Finally, an example is given to illustrate the effectiveness of the decision model.

In the DGM(1, 1) model modeling process, the influencing factors are uncertain. But the solution of DGM(1, 1) model with uncertain information is unique, which conflicts with the nonuniqueness principle of solution in grey theory. In view of this situation, this paper makes an in-depth analysis of the meaning of grey action quantity β₂ in DGM(1, 1) model and regards β₂ as an interval grey number. The maximum possibility whitenization value is given to estimate the kernel of grey number, and the typical possibility function is constructed to describe the possibility of grey number taking different values. A new DGM(1, 1) model with a grey parameter is then proposed, whose simulation results are interval grey numbers. The proposed model is compatible with the DGM(1, 1) model in model structure and simulation results. Finally, the practical example results show the applicability and effectiveness of the proposed model.

The aim of this paper is first to establish a general prediction framework for turning (period) term structures in COVID-19 epidemic related to the implementation of emergency risk management in the practice, which allows us to conduct the reliable estimation for the peak period based on the new concept of "${\mathbf{Turning~~ Period}}"$ (instead of the traditional one with the focus on "Turning Point") for infectious disease spreading such as the COVID-19 epidemic appeared early in year 2020. By a fact that emergency risk management is necessarily to implement emergency plans quickly, the identification of the Turning Period is a key element to emergency planning as it needs to provide a time line for effective actions and solutions to combat a pandemic by reducing as much unexpected risk as soon as possible. As applications, the paper also discusses how this "Turning Term (Period) Structure" is used to predict the peak phase for COVID-19 epidemic in Wuhan from January/2020 to early March/2020. Our study shows that the predication framework established in this paper is capable to provide the trajectory of COVID-19 cases dynamics for a few weeks starting from Feb.10/2020 to early March/2020, from which we successfully predicted that the turning period of COVID-19 epidemic in Wuhan would arrive within one week after Feb.14/2020, as verified by the true observation in the practice. The method established in this paper for the prediction of "${\mathbf{ Turning~~ Term ~~(Period) ~~Structures}}"$ by applying COVID-19 epidemic in China happened early 2020 seems timely and accurate, providing adequate time for the government, hospitals, essential industry sectors and services to meet peak demands and to prepare aftermath planning, and associated criteria for the Turning Term Structure of COVID-19 epidemic is expected to be a useful and powerful tool to implement the so-called "dynamic zero-COVID-19 policy" ongoing basis in the practice.

In view of the heterogeneity of natural gas consumption in different sectors in China, this paper utilizes Bayesian network (BN) to study the driving factors of natural gas consumption in power generation, chemical and industrial fuel sectors. Combined with Bayesian model averaging (BMA) and scenario analysis, the gas consumption of the three sectors is predicted. The results show that the expansion of urbanization will promote the gas consumption of power generation. The optimization of industrial structure and the increase of industrial gas consumption will enhance the gas consumption of chemical sector. The decrease of energy intensity and the increase of gas consumption for power generation will promote the gas consumption of industrial fuel. Moreover, the direct influencing factors of gas price are urbanization, energy structure and energy intensity. The direct influencing factors of environmental governance intensity are gas price, urbanization, industrial structure, energy intensity and energy structure. In 2025, under the high development scenario, China's gas consumption for power generation, chemical and industrial fuel sectors will be 66.034, 36.552 and 109.414 billion cubic meters respectively. From 2021 to 2025, the average annual growth rates of gas consumption of the three sectors will be 4.82%, 2.18% and 4.43% respectively.

We explore the association between the number of critical audit matters and auditing opinion based on the Chinese capital market data. In addition, we investigate the effect of audit risk on the relationship between the number of critical audit matters and auditing opinion. Using 6, 662 firm-year observations listed in the Chinese capital market from 2017—2019, we find that the number of critical audit matters has significantly positive association with clear audit opinion. Specially, the greater the number of critical audit matters, the more likely getting clear audit opinion. We also find that the audit risk restricts the effect of the critical audit matters on the audit opinion. Lastly, we find that Big4 accounting firms are less likely to be influenced by the number of critical audit matters; the audit complexity restricts the effect of the critical audit matters on the audit opinion; the more board members, the less effect of the number of critical audit matters on the audit opinion. We also use several robustness tests to strengthen the conclusion. Our research may contribute to the understanding of the new audit standard.

House price expectations (HPE) is a key factor affecting housing market fluctuations. Taking Beijing as an example, this study innovatively proposes an internet data-based measurement of HPE and text analysis-based quantification of media reports from media attention and media attitudes. Regression model is performed to empirically test the impact of media reports on the level and accuracy of HPE. The empirical results show no significant relationship between media attention and the level of HPE, but a significant relationship to the accuracy of HPE. The higher the media attention (i.e., the more intensive the media reports), the smaller the deviation between HPE and actual housing prices. The attitude of media reports is significantly related to the level and accuracy of HPE. It is easier to guide the formation of HPE through media reports with clear opinions, indicating that media could promote the sustainable development of the real estate market.

To reduce the flicker artifacts caused by video defogging, a surveillance video defogging algorithm based on the background extraction and consistent constraints is proposed. First, an inter frame consistency constraint is constructed and applied to background modeling. Second, the extracted background is defogged with an improved static defogging approach. Third, the foreground is extracted using the extracted background and further defogged using constraints of the consistency between the foreground and background. Experimental results show that our algorithm can remove fog effectively and preserve the temporal coherence well.

Considering diseconomies of scale and network externalities in the e-commerce supply chain (ECSC), we construct an e-platform-led benchmark model and derive the optimal decisions. Then, the model is extended by endogenizing the impact of service level on network externalities. Considering service investment that includes fixed and variable investments, the model is further extended. Comparing the extended models with the benchmark model, we found the following conclusions. Although the e-platform dominates the ECSC, its profit is lower than the manufacturer. The corporate profits, service level, and price increase with network externalities. Increases in diseconomies of scale decrease the corporate profits and service level, but increase the price. A high-quality service combined with network externalities can achieve synergy and improve the e-platform's economies of scale, further generating a higher profit. Improving network externalities promotes the fair profit distributionin ECSC and achieves stable development.

Under the background of population aggregation in megacities, some adjustments are made to the urbanization strategy, whose focus is shifted to the development of megacities and megacity clusters. Meanwhile, the housing price differentiation among cities tends to become increasingly serious. This paper, from the perspective of population mobility, takes provincial capitals and municipalities with independent planning status (PCs & MIPSs) as research samples to evaluate the level of housing price differentiation within provincial-level administrative divisions of China, and analyze from the perspective of demand side how the metropolitanization effects regarding the population formed due to population aggregation in megacities affect the housing prices of megacities and the housing price difference between megacities and other cities. The research found that: 1) The increasing net inflow of population boosts the housing prices and accelerate the housing price differentiation; 2) The impact of the increasing net inflow of population on housing price increases and housing price differentiation has regional heterogeneity and city size heterogeneity; 3) The income gap strengthens the effect of population inflow upon the housing price differentiation.

The common models used for grey system predictions include the GM(1, 1), the GM(1, N), the GM(N, 1), and so on, but their whitening equations are all single ordinary differential equations. However, objects and factors generally form a whole through mutual restrictions and connections when the objective world is developing and changing continuously. In other words, variables affect each other. The relationship can't be properly reflected by the single differential equation. Therefore, the paper proposes a novel simultaneous grey model. The paper gives a modeling method of simultaneous grey model SGM(1, 2) with 2 interactive variables. The example proves that the simultaneous grey model has high precision and improves the precision significantly compared with the conventional single grey model. The new method proposed enriches the grey modeling method system and has important significance for the in-depth study, popularization and application of grey models.

Based on a questionnaire data from 553 cities in China, this study used logistic regression to examine the effects of age, education, gender, occupation, and region on intercity talent mobility. The results revealed that individuals aged 26~45 years with work experience are more willing to relocate compared with most college students or individuals with little work experience. Furthermore, individuals who have acquired a bachelor's degree are willing to relocate, whereas those who have acquired a master's degree and above are less willing to relocate. In addition, cities offer a higher pay and better prospects for highly qualified individuals, so mobility seems less likely happen. Moreover, intercity mobility is higher for scientific research institutions than that for other industries. Talents tend to flow from central and western cities to eastern cities. Factors determining intercity talent mobility differ from region to region. Therefore, local governments, especially in central and western cities, should actively conduct research on talent strategies while promoting the construction of the city's regional economy; formulate scientific policies on talent mobility; promote the reasonable flow of talent; and provide an effective talent pool for urban development.

This paper presents an imperfect maintenance strategy for the multi-component systems. The proposed maintenance strategy takes into account two types of maintenance actions, namely preventive maintenance (PM) and corrective maintenance (CM). The imperfect effect of PM is modeled on the basis of the hybrid hazard rate model. Meanwhile, a new structure importance measure based on the survival signature is presented. Using this new importance measure method, an adjustment function is designed to update the PM maintenance threshold. For CM actions, a decision rule relying on the criticality level of components is introduced. In order to judge the criticality level of components, a novel structure updating method based on the survival signature is proposed. Moreover, a maintenance model considering economic dependence among components is developed. A 10-component system is finally introduced to illustrate the use and advantages of the proposed maintenance strategy.

Decision tree (DT) plays an important role in pattern recognition and machine learning, which is widely used for regression tasks because of its natural interpretability. Nevertheless, the traditional decision tree is constructed by recursive Boolean division. The discrete decision-making process in DT makes it non-differentiable, and causes the problem of hard decision boundary. To solve this problem, a probability distribution model — Staired-Sigmoid is proposed in this paper. The Staired-Sigmoid model is used to differentiate the decision-making process, by which the samples can be assigned to two sub-trees more finely. Based on Staired-Sigmoid, we further propose the soft decision tree (SDT) for regression tasks, where the samples are assigned to different sub-nodes according to a continuous probability distribution. This process is differentiable, and all parameters in SDT can be optimized by gradient descent algorithms. Owing to its constructing rules, SDT is more stable than decision tree, and it is easier to overcome the problem of overfitting. We validate SDT on several datasets obtained from UCI. Experiments demonstrate that SDT achieves better performance than decision tree, and it significantly alleviates the overfitting.

This research examines the effects of commercial insurance on household financial vulnerability using data from the China Household Finance Survey (CHFS). Data were collected from 39875 households in 29 provinces of China. The probit model was used to test the relationship between the study variables. The results show that commercial insurance participation reduces the likelihood of a householdos financial vulnerability. Heterogeneity analysis found that commercial insurance participation had a more significant dampening effect on the financial vulnerability of households with low personal expenses, low-income, low human capital, rural areas, and the central and western regions, indicating that commercial insurance has a universal effect. This study offers several policy implications for combating household financial vulnerability. First, improving the commercial insurance protection system in both urban and rural areas could improve householdso risk management capacity. Second, establishing tax-rewarding policies to encourage households to participate in commercial insurance. Third, increasing the popularity of commercial insurance, particularly in rural areas, and exploring the rural commercial insurance market.

This paper explores the relationship between CEO age in target firms, earnings management, mergers and acquisitions decision-making, and performance by using a sample of Chinese firms from 2008 to 2017. We found that CEO age is negatively correlated with M&A decision-making and target firms engage in a higher degree accrual-based earnings management (AEM) than non-target firms. In addition, target firms with young CEOs exhibit a greater extent of AEM in the pre-M&A period. We also found that the relationship between CEO age and M&A performance is inverted U-shaped. AEM of pre-M&A is negatively correlated with M&A performance, indicating that M&A performance is affected by AEM of pre-M&A.

This paper sheds light on the demand-side impact of China's emergence on the world economy from a value chain perspective. A measure linking final demand with value-added in the context of global value chains is developed. Data from the World Input-Output Database is used to analyze foreign value-added induced by final demand in China. It is found that foreign value-added to meet final demand in China increases over time except during 2008-2009, proving that China has a significant power to shape the world economy through its final demand. Besides, it is found that about 88% of foreign value-added is absorbed in China through intermediate imports. In addition, how and to what extend the final demand in China contributes to other countries' GDP growth is investigated. Results show that continuous increase in foreign value-added absorbed in China becomes a ballooning source of economic growth, and final demand in China is rather a remedy in aftermath of the global recession during 2008-2009. Finally, it is found that the increase in foreign value-added absorbed in China is due not only to the surge in final demand but also to bullwhip effect along global value chains.

In this paper, we study the optimal procurement management by reverse auctions for a price-setting newsvendor (retailer) in a single period setting. The retailer facing price-dependent stochastic demand first designs a procurement contract and then invites the suppliers to bid for this contract in the reverse auction. The winning supplier produces and delivers the demanded quantity. The retailer obtains the procurement quantity and simultaneously determines the retail price. By using the price elasticity of the lost-sales rate, we show that the retailer's expected profit (excluding the procurement cost) is a concave function of the purchased quantity, which can be used to obtain the optimal procurement and retail pricing decisions for the retailer. Further, when the underlying random term of demand function is normally distributed under left-truncation (at 0), we get the analytical expressions of the purchased quantity and expected profit function for the retailer. Moreover, some numerical examples are given.

In view of the breakthrough progress of the depth learning method in image and other fields, this paper attempts to introduce the depth learning method into stock price forecasting to provide investors with reasonable investment suggestions. This paper proposes a stock prediction hybrid model named ISI-CNN-LSTM considering investor sentiment based on the combination of long short-term memory (LSTM) and convolutional neural network (CNN). The model adopts an end-to-end network structure, using LSTM to extract the temporal features in the data and CNN to mine the deep features in the data can effectively improve the prediction ability of the model by increasing investor sentiment in the network structure. The empirical part makes a comparative experimental analysis based on Shanghai stock index in China. By comparing the experimental prediction results and evaluation indicators, it verifies the prediction effectiveness and feasibility of ISI-CNN-LSTM network model.

With continuous development of network technology, users in network community are promoted to interact deeply, and remarkable web collective intelligence emerges in the process. As a relatively new concept, the connotation of web collective intelligence is preliminarily explored in this paper, where the network community is taken as the environment, expert users as the subject, and web comments as the carrier. Meanwhile, taking Wikipedia as an example, by means of questionnaire survey and structural equation model, a more systematic index system is constructed from the perspective of user characteristics to explore determinants of web collective intelligence quality, and potential influence of user attributes on user behavior.

The port logistics capacity of countries along the Belt and Road is of great significance to promoting Chinese overseas comprehensive supporting system. The study combines quality data with numerical data to extract the key factors affecting the port logistics development for countries along the Belt and Road. Based on grey correlation analysis and factor analysis, 17 major ports of these countries are evaluated from 11 key factors. The results show that infrastructure, transportation, and entering and leaving costs are crucial for the development of port logistics. The research provides scientific decision-making basis for the construction of an overseas comprehensive supporting system under the context of the Belt and Road Initiative.