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Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.

To improve the accuracy of stock price trend prediction in the field of quantitative financial trading, this paper takes the prediction accuracy as the goal and avoid the enormous number of network structures and hyperparameter adjustments of long-short-term memory (LSTM).

In this paper, an adaptive genetic algorithm based on individual ordering is used to optimize the network structure and hyperparameters of the LSTM neural network automatically.

The simulation results show that the accuracy of the rise and fall of the stock outperform than the model with LSTM only as well as other machine learning models. Furthermore, the efficiency of parameter adjustment is greatly higher than other hyperparameter optimization methods.

(1) The AGA-LSTM algorithm is used to input various hyperparameter combinations into genetic algorithm to find the best hyperparameter combination. Compared with other models, it has higher accuracy in predicting the up and down trend of stock prices in the next day. (2) Adopting real coding, elitist preservation and self-adaptive adjustment of crossover and mutation probability based on individual ordering in the part of genetic algorithm, the algorithm is computationally efficient and the results are more likely to converge to the global optimum.

The purpose of this paper is to reveal the solute segregation behavior in the molten and solidified regions during direct chill (DC) casting of a large-size magnesium alloy slab under no magnetic field (NMF), harmonic magnetic field (HMF), pulsed magnetic field (PMF) and two types of out-of-phase pulsed magnetic field (OPMF).

A 3-D multiphysical coupling mathematical model is used to evaluate the corresponding physical fields. The coupling issue is addressed using the method of separating step and result inheritance.

The results suggest that the solute deficiency tends to occur in the central part, while the solute-enriched area appears near the fillet in the molten and solidified regions. Applying magnetic field could greatly homogenize the solute field in the two-phase region. The variance of relative segregation level in the solidified cross-section under NMF is 38.1%, while it is 21.9%, 18.6%, 16.4% and 12.4% under OPMF2 (the current phase in the upper coil is ahead of the lower coil), HMF, PMF and OPMF1 (the current phase in the upper coil lags behind the lower coil), respectively, indicating that OPMF1 is more effective to reduce the macrosegregation level.

There are few reports on the solute segregation degree in rectangle slab under magnetic field, especially for magnesium alloy slab. This paper can act a reference to make clear the solute transport behavior and help reduce the macrosegregation level during DC casting.

The purpose of this paper is to investigate the effect of pulsed magnetic field (PMF) with different duty cycles on the melt flow and heat transfer behaviors during direct-chill (DC) casting of large-size magnesium alloy billet and find the appropriate range of duty cycle.

A transient two-dimensional mathematical model coupled electromagnetic field, flow field and thermal field, is conducted to study the melt flow and temperature field under PMF and compared with that under the harmonic magnetic field.

The results reveal that melt vibration and fluctuation are generated due to the instantaneous impact of repeated thrust and pull effects of Lorentz force under PMF. The peak of Lorentz force decreases greatly with the increasing duty cycle, but the melt fluctuation region is expanded with higher duty cycle, which accelerates the interior melt velocity and reduces the temperature gradient at the liquid-solid interface. However, PMF with overly high duty cycle has adverse effect on the melt convection and limited influence on the interior melt. A duty cycle of 20% to 50% is a reasonable range.

This paper can provide guiding significance for the setting of duty cycle parameters on DC casting under PMF.

There are few reports on the effect of PMF parameters during DC casting with applying PMF, especially for duty cycle, a parameter unique to PMF. The findings will be helpful for applying the external field of PMF on DC casting.

There has long been a belief that authentic leadership positively impacts employee outcomes, consistently proven to be effective. However, is this conclusion universally applicable in any situation? To answer this question, we investigated the interaction effect between authentic leadership and two types of team conflict (task conflict and relationship conflict) on employee creativity as well as the mediating role of employee trust in the leader (affective-based and cognitive-based), looking to open up a new milestone for authentic leadership.

This study involved 589 employees across 75 teams, with data collected using a dual-wave, multi-source approach.

The results demonstrated that authentic leadership can enhance employee creativity, but this effect is not absolute. Specifically, the interaction between authentic leadership and task conflict negatively affected employee creativity, with this relationship mediated by cognitive-based trust. Conversely, there was no significant impact of authentic leadership on relationship conflict affecting creativity, nor was this relationship mediated by affective-based trust.

This study challenges the long-held belief that authentic leadership always positively impacts employee creativity. Moreover, it provides new perspectives on the different situations in which authentic leadership has varying impacts on employee creativity. Additionally, the study contributes to a further systematic and comprehensive understanding of the impact of authentic leadership.

This paper examines the impact of the COVID-19 pandemic on low-carbon consumption of dairy products through informational interventions. The empirical findings seek to enlighten developing countries' efforts in coping with climate change and potential dietary transitions.

A randomized controlled trial was designed to examine the effects of purpose-differentiated information interventions on individual dairy consumption. The experiment recruited and randomly assigned 1,002 college students into four groups to receive (or not) environmental or/and health information interventions.

The empirical analysis finds that health and combined information interventions have a positive impact on dairy consumption, while environmental information interventions' effect on dairy consumption is insignificant. In the context of the pandemic, health information interventions positively affected participants' perceptions and preferences for dairy products by delivering knowledge about their role in boosting immunity. However, environmental information interventions failed to do the same things as their insignificant effects on both perception and preference.

Macro-external shocks, such as public health events, may offset the impact of universal information interventions promoting pro-environmental behaviors. For a smooth dietary transition to achieve long-term environmental sustainability, diverse stakeholders must be included in more individualized interventions to guide daily consumption, especially in developing countries with large populations.

The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO₂ emissions, farming areas and the labor force in China.

This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production.

The Johansen test findings have shown that the fruit crop growth, energy use, CO₂ emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO₂ emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO₂ will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO₂ emissions to agricultural areas to fruit crops.

This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.

The purpose of this paper is to investigate the innovation capabilities of Chinese enterprises. It is important to understand these innovation capabilities and to find how they might relate to promoting innovativeness.

In this paper, data obtained from a survey of enterprises in the Chinese provinces Liaoning and Henan are analysed using multiple regression to ascertain how facets of innovation capabilities might enhance the likelihood of innovation success. Analysis is conducted for innovation defined as incremental, radical and the total of both.

It is found that focusing on customers tends to reduce innovation for Chinese companies, whilst competitor orientation, knowledge enhancement and management capability are associated with enhanced innovative ability.

There are many determinants of innovation, notably as skills and attributes of the individual entrepreneur. In this paper the focus is on organisational facets and not on those of the individual. The paper shows the importance of monitoring competitors, as well as managing and enhancing knowledge.

The paper reports the analysis and extent of different capabilities for successful innovation from a unique data set from two provinces in China.

The purpose of this study is to investigate the determinants that determine the investment behaviour of rural farmers. This study further examines the moderation effect of socio traits in the association between investment behaviour and its determined factors.

This study used a cross-sectional research design to gather information. The information for this research survey was gathered using a structured questionnaire from 400 individual investors in the rural area of Punjab, who participated in the study. It has been decided to use the Cronbach’s alpha test to determine the validity and reliability of the questionnaire. To evaluate the hypothesis, structural equation modelling has been used in the research process.

The results of this study reveal that attitude, financial risk inclination, financial planning and investment intention determine the investment behaviour of the rural people of Punjab. The results for the interaction effect of socio traits with investment intention, financial risk propensity and investment attitude were found statistically significant amongst rural people. The results of the moderation effect stated that interaction between the attitude and investment intention and financial risk propensity and investment intention is significantly influenced by age of respondents. The results further reveal that marital status of rural people affect the interaction between attitude and investment intention and financial risk propensity and investment intention. Nothing about education seems to be a moderating influence on any of the relationships studied.

The authors contribute to the literature in two aspects. Firstly, to the best of the authors’ knowledge, this is the only study of its kind that focuses on the investment behaviour of farmers. Secondly, by looking at the farmer’s investing behaviour, the moderation effect of demographic variables is also studied which set this study apart from another existing scholarly research. This study contributes to the growing literature on investment behaviour of farmers in developing and developed markets.