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In recent years, continuous expansion of the scale of the new energy export industry in China caused a boycott of American and European countries. Export injury early warning research is an urgent task to develop the new energy industry in China. The purpose of this paper is to build an indicator system of exports injury early warning of the new energy industry in China and corresponding quantitative early warning models.

In consideration of the actual condition of the new energy industry in China, this paper establishes an indicator system according to four aspects: export price, export quantity, impact on domestic industry and impact on macro economy. Based on the actual data of new energy industry and its five sub-industries (solar, wind, nuclear power, smart grid and biomass) in China from 2003 to 2013, GM (1,1) model is used to predict early warning index values for 2014-2018. Then, the principal component analysis (PCA) is used to obtain the comprehensive early warning index values for 2003-2018. The 3-sigma principle is used to divide the early warning intervals according to the comprehensive early warning index values for 2003-2018 and their standard deviation. Finally, this paper determines alarm degrees for 2003-2018.

Overall export condition of the new energy industry in China is a process from cold to normal in 2003-2013, and the forecast result shows that it will be normal from 2014 to 2018. The export condition of the solar energy industry experienced a warming process, tended to be normal, and the forecast result shows that it will also be normal in 2014-2018. The biomass and other new energy industries and nuclear power industry show a similar development process. Export condition of the wind energy industry is relatively unstable, and it will be partially hot in 2014-2018, according to the forecast result. As for the smart grid industry, the overall export condition of it is normal, but it is also unstable, in few years it will be partially hot or partially cold. The forecast result shows that in 2014-2018, it will maintain the normal state. In general, there is a rapid progress in the export competitiveness of the new energy industry in China in the recent decade.

Export injury early warning research of the new energy industry can help new energy companies to take appropriate measures to reduce trade losses in advance. It can also help the relevant government departments to adjust industrial policies and optimize the new energy industry structure.

This paper constructs an index system that can measure the alarm degrees of the new energy industry. By combining the GM (1,1) model and the PCA method, the problem of warning condition detection under small sample data sets is solved.

Using Chinese A-share listed firms between 2007 and 2020 with 21,380 observations, we aim to examine the impact of cross-ownership on firms’ innovation output and explore the underlying mechanisms.

To test the influence of cross-ownership on firms’ innovation output, this paper constructs an ordinary least square regression model. The explained variables are firms’ innovation output, including the total number of patent applications (Apply) and the number of invention patent applications (Apply_I). Considering the long period of patent R&D, we take the value of the explained variables in the following year for regression. Cross-ownership (Cross) is the explanatory variable; Control is the control variable; and ε is the regression residual term.

We find that cross-ownership significantly promotes corporate innovation output, indicating that cross-owners play an important role in “collaborative governance.” This finding remains unchanged after conducting a series of robustness tests. We also find that cross-ownership contributes to innovation output mainly through two plausible channels: the relaxation of financing constraints and reducing both types of agency costs. Further analysis shows that cross-ownership has a more pronounced influence on innovation output in those firms with higher equity restriction ratios and facing more competitive markets. Moreover, cross-ownership has a profound impact on firms’ innovation quality and innovation efficiency, thereby increasing firm value.

This study provides important policy implications. First, cross-owners should actively play their resource and supervision advantages to improve firms’ long-term development capability through the “collaborative governance” effect. Second, listed companies in China should be fully aware of the value of the cross-ownership and use the cross-ownership as a bridge to strengthen the cooperative relationship with firms in the same portfolio. Meanwhile, they need to pay attention to cross-ownership’s “collaborative governance” effect to provide an impetus for the healthy development of enterprises. Finally, government regulators should maintain appropriate supervision of the cross-ownership linkage in the market.

Our findings show that cross-ownership significantly contributes to firms’ innovation output, indicating that cross-owners play the role of “collaborative governance.” While paying attention to the collusion effect of the cross-ownership, they shall not ignore its governance effect, for example, the promotion effect on the innovation level. Government regulators should appropriately supervise the cross-ownership linkage, which is conducive to maintaining the market order and driving the healthy development of the capital market.

The purpose of this paper is to propose an availability modeling method of complex multiple units system (CMUS) based on the multi-agent technique.

Based on the multi-agent technique, this paper describes the availability model structure for CMUS and develops agent-based models of components, maintenance policies, maintenance tools, maintenance fields, and maintenance staff, as well as the communication method among the different agents. On the basis of the agent-based availability modeling theory, the availability simulation scheme of CMUS is given using MATLAB. Thus, the availability modeling theory of CMUS and its simulation method are developed. To demonstrate the applicability of the proposed availability modeling method, a numerical example is given.

The proposed agent-based modeling method is applicable to availability modeling of CMUS, including the modeling of component failure, maintenance tools/fields/staff, maintenance policy, and structural/economic dependence among components.

As a bottom-top, modular, expandable, and reusable modeling theory, the agent-based modeling method might be useful for availability modeling of different CMUSs in reality.

The multi-agent technique is introduced into availability modeling of multi-component systems in this paper. Thus, it is possible to model failure of many components, maintenance policies, maintenance tools, maintenance fields, and maintenance staff together for availability analysis of complex systems of equipment.

This study aims to explore the effect of urban green space (UGS) on residents' subjective well-being (SWB) among different social groups.

Using national SWB and UGS data obtained from the China Urban Construction Statistical Yearbook and the Chinese General Social Survey, a multiple regression model was developed to estimate the effect of UGS on residents' SWB. Grouping regression for samples from distinct socioeconomic groups was performed to further discuss group-wise differences in SWB.

The green coverage rate of built-up areas and the number of parks accessed by every 10,000 individuals are significantly positively correlated with residents' SWB, whereas the green space area per capita and greening investment ratio are significantly negatively correlated with residents' SWB; the effect of UGS on residents' SWB varies among individuals with respect to gender, age, and income, with the most significant difference observed among groups with different incomes.

The empirical results of this study are expected to support the planning and construction of UGS by providing a reference for optimizing their service capabilities and highlighting their positive role in improving residents' SWB.

Decision-making problems in emergency plan selection for epidemic prevention and control (EPAC) are generally characterized by risky and uncertainty due to multiple possible emergency states and vagueness of decision information. In the process of emergency plan selection for EPAC, it is necessary to consider several obvious features, i.e. different states of epidemics, dynamic evolvement process of epidemics and decision-makers' (DMs') psychological factors such as risk preference and loss aversion.

In this paper, a novel decision-making method based on cumulative prospect theory (CPT) is proposed to solve emergency plan selection of an epidemic problem, which is generally regarded as hybrid-information multi-attribute decision-making (HI-MADM) problems in major epidemics. Initially, considering the psychological factors of DMs, the expectations of DMs are chosen as reference points to normalize the expectation vectors and decision information with three different formats. Subsequently, the matrix of gains and losses is established according to the reference points. Furthermore, the prospect value of each alternative is obtained and the comprehensive prospect values of alternatives under different states are calculated. Accordingly, the ranking of alternatives can be obtained.

The validity and robustness of the proposed method are demonstrated by a case calculation of emergency plan selection. Meanwhile, sensitivity analysis and comparison analysis with fuzzy similarity to ideal solution (FTOPSIS) method and TODIM (an acronym in Portuguese for interactive and MADM) method illustrate the effectiveness and superiority of the proposed method.

An emergency plan selection method is proposed for EPAC based on CPT, taking into account the psychological factors of DMs.

Highlights

1. This paper proposes a new CPT-based EDM method for EPAC under a major epidemic considering the psychological factorsof DMs, such as risk preference, loss aversion and so on.

2. The authors' work gives approaches of normalization, comparison and distance measurement for dealing with the integration of three hybrid formats of attributes.

3. This article gives some guidance, which contributes to solve the problems of risk-based hybrid multi-attribute EDM.

4. The authors illustrate the advantages of the proposed method by a sensitivity analysis and comparison analysis with existing FTOPSIS method and TODIM method.

This paper proposes a new CPT-based EDM method for EPAC under a major epidemic considering the psychological factorsof DMs, such as risk preference, loss aversion and so on.

The authors' work gives approaches of normalization, comparison and distance measurement for dealing with the integration of three hybrid formats of attributes.

This article gives some guidance, which contributes to solve the problems of risk-based hybrid multi-attribute EDM.

The authors illustrate the advantages of the proposed method by a sensitivity analysis and comparison analysis with existing FTOPSIS method and TODIM method.

This research aims to examine which cooperative contract (wholesale-price contract or cost-sharing contract) can more effectively upgrade the green degree of product and promote demand when considering consumer reference price effect under different power structures.

This research investigates a dyadic green supply chain composed of one manufacturer and one retailer. Four Stackelberg game models with a cost-sharing contract or a wholesale-price contract are built in retailer-led and manufacturer-led scenarios, respectively. Using backward induction, the optimal green decision under each model is obtained. In addition, the optimal cooperative contract is proposed by comparing these four models.

It is found that under consumer reference price effect, a cost-sharing contract outperforms a wholesale-price contract in upgrading product greenness and promoting demand. Under any single contract, the retailer-led situation is more conducive to improving product greenness than the manufacturer-led situation. Moreover, consumer reference price effect would reduce the sharing ratio of a cost-sharing contract when the manufacturer dominates, but it could mitigate the problem of double marginalization by reducing wholesale and retail prices under both types of contracts, which would enhance consumer surplus.

It is a new attempt to incorporate consumer reference price effect and power structure into a green supply chain framework and proposes a novel demand function that simultaneously emphasizes consumer reference price effect, consumer environmental awareness and product green attribute. In addition, it provides managerial insights for business managers to choose green cooperative contracts with consumer reference price effect under different power structures.

As the complexity and uncertainty of infrastructural megaprojects challenge traditional management models, there is an increasing focus on value co-creation as an organizational strategy to streamline management. However, the role of value co-creation behavior in facilitating the value realization process remains underexplored. This study examines how justice perception (distributive, procedural and interactional justice) improves contractor value co-creation behavior, focusing on the mediating role of psychological ownership.

Ten hypotheses in the proposed research model were tested through partial least squares structural equation modeling using 199 valid questionnaires from China.

The results show that contractor value co-creation behavior is directly and positively influenced by procedural, distributive and interactional justice and indirectly influenced by them through the underlying psychological mechanism of psychological ownership.

The findings fill a knowledge gap by examining the effect of justice perception on contractor value co-creation behavior based on social exchange theory. Discovering justice perception will contribute to contractor value co-creation behavior, and psychological ownership mediates this relationship.

Advertisements facilitate certain emotions, subsequently influencing purchase intentions. Humor, as an influential way of information expression, is frequently used in ads to elicit emotions. Drawing upon literature on advertisement humor and new product purchase intention and the theory of planned behavior, the study proposes that humor stimulation in advertisements can affect consumers' new product purchase intentions, in which two process mechanisms, namely, emotional arousal and cognitive flexibility, play a mediating effect.

To test the assertions, the authors conduct three experimental studies. The authors' first study assesses the main effect between advertisement humor and purchase intentions. In the second study, the authors show the mediating effects of emotional pleasure, emotional arousal, and cognitive flexibility on the relationship between advertisement humor and purchase intentions. In the first two experiments, the authors study incremental new products. In the third study, the authors study the same mediating relationships for radically new products.

This study's results show consumers that watch humorous ads are more likely to choose new products than those who watch non-humorous ads (Study 1); compared with non-humorous ads, humorous ads can enhance emotional arousal, thus promoting cognitive flexibility and making consumers more inclined to choose new products (Study 2 and Study 3). That said, the authors find that these mediation effects are only partial.

This study's results have important implications for firms vying to enhance consumers' new product purchase intentions by deploying humorous ads.

Reinforced concrete slabs in fire have been heavily studied over the last three decades. However, most experimental and numerical work focuses on long-duration uniform exposure to standard fire. Considerably less effort has been put into investigating the response to localised fires that result in planarly non-uniform temperature distribution in the exposed elements.

In this paper, the OpenSees for Fire framework for modelling slabs under non-uniform fire exposure is presented, verified against numerical predictions by Abaqus and then validated against experimental tests. The thermal wrapper developed within OpenSees for Fire is then utilised to apply localised fire exposure to the validated slab models using the parameters of an experimentally observed localised fire. The effect of the smoke layer is also considered in this model and shown to significantly contribute to the thermal and thus thermo-mechanical response of slabs. Finally, the effect of localised fire heat release rate (HRR) and boundary conditions are studied.

The analysis showed that boundary conditions are very important for the response of slabs subject to localised fire, and expansive strains may be accommodated as deflections without severely damaging the slab by considering the lateral restraint.

This work demonstrates the capabilities of OpenSees for Fire in modelling structural behaviours subjected to non-uniform fire conditions and investigates the damage pattens of flat slabs exposed to localised fires. It is an advancing step towards understanding structural responses to realistic fires.

The purpose of this paper is to analyze the mechanism and filter efficacy of accumulation generation operator (AGO)/inverse accumulation generation operator (IAGO) in the frequency domain.

The AGO/IAGO in time domain will be transferred to the frequency domain by the Fourier transform. Based on the consistency of the mathematical expressions of the AGO/IAGO in the gray system and the digital filter in digital signal processing, the equivalent filter model of the AGO/IAGO is established. The unique methods in digital signal processing systems “spectrum analysis” of AGO/IAGO are carried out in the frequency domain.

Through the theoretical study and practical example, benefit of spectrum analysis is explained, and the mechanism and filter efficacy of AGO/IAGO are quantitatively analyzed. The study indicated that the AGO is particularly suitable to act on the system's behavior time series in which the long period parts is the main factor. The acted sequence has good effect of noise immunity.

The AGO/IAGO has a wonderful effect on the processing of some statistical data, e.g. most of the statistical data related to economic growth, crop production, climate and atmospheric changes are mainly affected by long period factors (i.e. low-frequency data), and most of the disturbances are short-period factors (high-frequency data). After processing by the 1-AGO, its high frequency content is suppressed, and its low frequency content is amplified. In terms of information theory, this two-way effect improves the signal-to-noise ratio greatly and reduces the proportion of noise/interference in the new sequence. Based on 1-AGO acting, the information mining and extrapolation prediction will have a good effect.

The authors find that 1-AGO has a wonderful effect on the processing of data sequence. When the 1-AGO acts on a data sequence X, its low-pass filtering effect will benefit the information fluctuations removing and high-frequency noise/interference reduction, so the data shows a clear exponential change trends. However, it is not suitable for excessive use because its equivalent filter has poles at the non-periodic content. But, because of pol effect at zero frequency, the 1-AGO will greatly amplify the low-frequency information parts and suppress the high-frequency parts in the information at the same time.