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This paper seeks to examine how leaders shape employee creativity by using interpersonal emotion management (IEM) strategies. Drawing on the social information processing (SIP) theory, the authors argue that psychological safety translates leader problem-focused IEM into employee creativity, an impact which is moderated by organizational justice.

Data were collected in two waves from 201 employees and their leaders in China. Regression analysis was used to test the hypotheses.

Leader problem-focused IEM is positively related to employee creativity, and this relationship is mediated by psychological safety. Organizational justice positively moderates the relationship between leader problem-focused IEM and psychological safety as well as the indirect relationship between leader problem-focused IEM and employee creativity via psychological safety.

This paper identifies a novel and useful predictor of employee creativity from the perspective of leader problem-focused IEM and provides practical insights for organizations regarding ways of improving employee creativity.

Digital transformation misalignment refers to disparities in digital transformation levels between suppliers and buyers across the production and operation process. It has negatively affected supply chain stability. However, the existing research concerning the economic consequences has not been adequately addressed. Therefore, this paper aims to investigate whether such digital transformation misalignment increases supplier financial risk and to identify the factors influencing this relationship.

This paper examines binary combinations of suppliers and buyers listed on China’s A-share market between 2011 and 2021. This group constitutes a sample to empirically test the influence of digital transformation misalignment on the supplier’s financial risk, as well as the moderating effect of the geographical and organizational distances.

The paper’s findings demonstrate that digital transformation misalignment has indeed a significant increase in the supplier’s financial risk. Moreover, the impact is more intense when the geographical or organizational distance between the supplier and the buyer is relatively large.

The existing literature rarely explores the potential risks arising from digital transformation misalignment between supply chain partners. Therefore, this paper fills a notable gap as it is the first to study the impact of digital transformation misalignment on the supplier’s financial risk and the specific applied mechanisms. The contribution significantly improves the field of corporate digital transformation, particularly, within the context of supply chain management.

This paper aims to examine the association between the dietary inflammatory index, the consumption of Enteral Nutrition Supplemented with probiotics with certain serum inflammation markers and gastrointestinal complications among individuals diagnosed with COVID-19.

This cross-sectional investigation involved 100 COVID-19 patients who were admitted to intensive care units in hospitals. These patients were administered two different types of Enteral Nutrition, so the dietary inflammatory index (DII), gastrointestinal complications and some serum inflammation markers have been compared between two groups.

The mean DII scores in all patients were significantly pro-inflammatory (probiotic formula 2.81 ± 0.01 vs usual formula group 2.93 ± 0.14 p = 0.19). The probiotic formula consumption had an inverse association with High-sensitivity C-reactive Protein concentration (coef = −3.19, 95% CI −1.25, −5.14 p = 0.001) and lead to a reduction of 2.14 mm/h in the serum level of Erythrocyte sedimentation rate compared to normal formula. The incidence of diarrhea, abdominal pain and vomiting in probiotic formula patients was respectively 94%, 14% and 86% less than in usual formula patients (p = 0.05).

In this cross-sectional study for the first time, the authors found that probiotic formula consumption was inversely associated with serum inflammation markers and gastrointestinal complications incidence. The high DII leads to more gastrointestinal complications incidence and inflammation markers. More studies are needed to prove this relationship.

This study aims to enhance the prediction accuracy of hydroelectricity consumption in China, with a focus on addressing the challenges posed by complex and nonlinear characteristics of the data. A novel grey multivariate prediction model with structural optimization is proposed to overcome the limitations of existing grey forecasting methods.

This paper innovatively introduces fractional order and nonlinear parameter terms to develop a novel fractional multivariate grey prediction model based on the NSGM(1, N) model. The Particle Swarm Optimization algorithm is then utilized to compute the model’s hyperparameters. Subsequently, the proposed model is applied to forecast China’s hydroelectricity consumption and is compared with other models for analysis.

Theoretical derivation results demonstrate that the new model has good compatibility. Empirical results indicate that the FMGM(1, N, a) model outperforms other models in predicting the hydroelectricity consumption of China. This demonstrates the model’s effectiveness in handling complex and nonlinear data, emphasizing its practical applicability.

This paper introduces a scientific and efficient method for forecasting hydroelectricity consumption in China, particularly when confronted with complexity and nonlinearity. The predicted results can provide a solid support for China’s hydroelectricity resource development scheduling and planning.

The primary contribution of this paper is to propose a novel fractional multivariate grey prediction model that can handle nonlinear and complex series more effectively.

This paper addresses the challenges associated with forecasting electricity consumption using limited data without making prior assumptions on normality. The study aims to enhance the predictive performance of grey models by proposing a novel grey multivariate convolution model incorporating residual modification and residual genetic programming sign estimation.

The research begins by constructing a novel grey multivariate convolution model and demonstrates the utilization of genetic programming to enhance prediction accuracy by exploiting the signs of forecast residuals. Various statistical criteria are employed to assess the predictive performance of the proposed model. The validation process involves applying the model to real datasets spanning from 2001 to 2019 for forecasting annual electricity consumption in Cameroon.

The novel hybrid model outperforms both grey and non-grey models in forecasting annual electricity consumption. The model's performance is evaluated using MAE, MSD, RMSE, and R², yielding values of 0.014, 101.01, 10.05, and 99% respectively. Results from validation cases and real-world scenarios demonstrate the feasibility and effectiveness of the proposed model. The combination of genetic programming and grey convolution model offers a significant improvement over competing models. Notably, the dynamic adaptability of genetic programming enhances the model's accuracy by mimicking expert systems' knowledge and decision-making, allowing for the identification of subtle changes in electricity demand patterns.

This paper introduces a novel grey multivariate convolution model that incorporates residual modification and genetic programming sign estimation. The application of genetic programming to enhance prediction accuracy by leveraging forecast residuals represents a unique approach. The study showcases the superiority of the proposed model over existing grey and non-grey models, emphasizing its adaptability and expert-like ability to learn and refine forecasting rules dynamically. The potential extension of the model to other forecasting fields is also highlighted, indicating its versatility and applicability beyond electricity consumption prediction in Cameroon.

This paper aims to develop a novel grey Bernoulli model with memory characteristics, which is designed to dynamically choose the optimal memory kernel function and the length of memory dependence period, ultimately enhancing the model's predictive accuracy.

This paper enhances the traditional grey Bernoulli model by introducing memory-dependent derivatives, resulting in a novel memory-dependent derivative grey model. Additionally, fractional-order accumulation is employed for preprocessing the original data. The length of the memory dependence period for memory-dependent derivatives is determined through grey correlation analysis. Furthermore, the whale optimization algorithm is utilized to optimize the cumulative order, power index and memory kernel function index of the model, enabling adaptability to diverse scenarios.

The selection of appropriate memory kernel functions and memory dependency lengths will improve model prediction performance. The model can adaptively select the memory kernel function and memory dependence length, and the performance of the model is better than other comparison models.

The model presented in this article has some limitations. The grey model is itself suitable for small sample data, and memory-dependent derivatives mainly consider the memory effect on a fixed length. Therefore, this model is mainly applicable to data prediction with short-term memory effect and has certain limitations on time series of long-term memory.

In practical systems, memory effects typically exhibit a decaying pattern, which is effectively characterized by the memory kernel function. The model in this study skillfully determines the appropriate kernel functions and memory dependency lengths to capture these memory effects, enhancing its alignment with real-world scenarios.

Based on the memory-dependent derivative method, a memory-dependent derivative grey Bernoulli model that more accurately reflects the actual memory effect is constructed and applied to power generation forecasting in China, South Korea and India.

Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.

The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.

Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.

Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.

This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.

Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.

In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.

The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.

To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.

This study aims to explore the connection between travel content consumption in social networks and social comparison, envy as well as travel intention. It analyzes whether tourists’ travel intention is affected by travel content consumption in social networks, and more importantly, whether social comparison and envy play a mediating role in this process.

Data was collected through intercept in four popular tourist spots in Guangzhou and Zhuhai in South China. A self-administered questionnaire was used. A total of 400 participants were recruited, and 291 valid questionnaires were obtained. Bias-corrected nonparametric percentile bootstrap mediation variable test method was used to test hypotheses.

The study yielded three results. First, travel content consumption in the social networks positively influences travel intention. Second, travel content consumption in social networks indirectly affects travel intention through social comparison and envy. Third, the control variables, such as gender, age, education and income, mainly affect envy.

This study constructs a theoretical framework of stimulus–cognitive appraisal–emotion–behavioral responses. To the best of the authors’ knowledge, it is the first study to reveal that the internal psychological mechanism of travel content consumption affects travel intention. It also discloses that envy of seemingly negative emotions can encourage positive behaviors in certain situations.

When the reinforced concrete beams are reinforced by bonding steel plates to the bottom, excessive use of steel plates will make the reinforced concrete beams become super-reinforced beams, and there are security risks in the actual use of super-reinforced beams. In order to avoid the occurrence of this situation, the purpose of this paper is to study the calculation method of the maximum number of bonded steel plates to reinforce reinforced concrete beams.

First of all, when establishing the limit failure state of the reinforced member, this paper comprehensively considers the role of the tensile steel bar and steel plate and takes the load effect before reinforcement as the negative contribution of the maximum number of bonded steel plates that can be used for reinforcement. Through the definition of the equivalent tensile strength, equivalent elastic modulus and equivalent yield strain of the tensile steel bar and steel plate, a method to determine the relative limit compression zone height of the reinforced member is obtained. Second, based on the maximum ratio of (reinforcement + steel plate), the relative limit compression zone height and the equivalent tensile strength of the tensile steel bar and steel plate of the reinforced member, the calculation method of the maximum number of bonded steel plates is derived. Then, the static load test of the test beam is carried out and the corresponding numerical model is established, and the reliability of the numerical model is verified by comparison. Finally, the accuracy of the calculation method of the maximum number of bonded steel plates is proved by the numerical model.

The numerical simulation results show that when the steel plate width is 800 mm and the thickness is 1–4 mm, the reinforced concrete beam has a delayed yield platform when it reaches the limit state, and the failure mode conforms to the basic stress characteristics of the balanced-reinforced beam. When the steel plate thickness is 5–8 mm, the sudden failure occurs without obvious warning when the reinforced concrete beam reaches the limit state. The failure mode conforms to the basic mechanical characteristics of the super-reinforced beam failure, and the bending moment of the beam failure depends only on the compressive strength of the concrete. The results of the calculation and analysis show that the maximum number of bonded steel plates for reinforced concrete beams in this experiment is 3,487 mm². When the width of the steel plate is 800 mm, the maximum thickness of the steel plate can be 4.36 mm. That is, when the thickness of the steel plate, the reinforced concrete beam is still the balanced-reinforced beam. When the thickness of the steel plate, the reinforced concrete beam will become a super-reinforced beam after reinforcement. The calculation results are in good agreement with the numerical simulation results, which proves the accuracy of the calculation method.

This paper presents a method for calculating the maximum number of steel plates attached to the bottom of reinforced concrete beams. First, based on the experimental research, the failure mode of reinforced concrete beams with different number of steel plates is simulated by the numerical model, and then the result of the calculation method is compared with the result of the numerical simulation to ensure the accuracy of the calculation method of the maximum number of bonded steel plates. And the study does not require a large number of experimental samples, which has a certain economy. The research result can be used to control the number of steel plates in similar reinforcement designs.