Impostor leader and knowledge hiding: Attachment avoidance as underlying mechanism.

Impostorism and knowledge-hiding behaviors negatively impact employees and organizational performance. This study examines the association between impostor leaders and knowledge hiding (evasive hiding, playing dumb, and rationalized hiding). Attachment avoidance is discussed as a mediator between impostor leaders and knowledge-hiding. For quantitative analyses, this study collected the data from 429 individuals with two time lags by sharing the survey instrument link on different organizations' randomly selected official media pages. After obtaining approval from the administrators of these pages, leaders and subordinates from these organizations were asked to participate in the study. The partial least squares structural equation modeling method is employed with Smartpls-4 software for data analyses. The findings indicate that impostor leaders promote knowledge hiding in subordinates. However, impostor leaders highly promote rationalized hiding behavior in subordinates. Attachment avoidance mediates the relationship between the impostor leader and knowledge-hiding behaviors. However, the highest mediation relationship exists between an impostor leader and playing dumb behavior in subordinates. This study strengthens the generalizability of the social exchange theory. The implications mentioned in this study are beneficial in understanding and dealing with the Impostorism and knowledge-hiding phenomena.

MMDTA: A Multimodal Deep Model for Drug-Target Affinity with a Hybrid Fusion Strategy.

The prediction of the drug-target affinity (DTA) plays an important role in evaluating molecular druggability. Although deep learning-based models for DTA prediction have been extensively attempted, there are rare reports on multimodal models that leverage various fusion strategies to exploit heterogeneous information from multiple different modalities of drugs and targets. In this study, we proposed a multimodal deep model named MMDTA, which integrated the heterogeneous information from various modalities of drugs and targets using a hybrid fusion strategy to enhance DTA prediction. To achieve this, MMDTA first employed convolutional neural networks (CNNs) and graph convolutional networks (GCNs) to extract diverse heterogeneous information from the sequences and structures of drugs and targets. It then utilized a hybrid fusion strategy to combine and complement the extracted heterogeneous information, resulting in the fused modal information for predicting drug-target affinity through the fully connected (FC) layers. Experimental results demonstrated that MMDTA outperformed the competitive state-of-the-art deep learning models on the widely used benchmark data sets, particularly with a significantly improved key evaluation metric, Root Mean Square Error (RMSE). Furthermore, MMDTA exhibited excellent generalization and practical application performance on multiple different data sets. These findings highlighted MMDTA's accuracy and reliability in predicting the drug-target binding affinity. For researchers interested in the source data and code, they are accessible at http://github.com/dldxzx/MMDTA.

GHGPR-PPIS: A graph convolutional network for identifying protein-protein interaction site using heat kernel with Generalized PageRank techniques and edge self-attention feature processing block.

Accurately pinpointing protein-protein interaction site (PPIS) on the molecular level is of utmost significance for annotating protein function and comprehending the mechanisms underpinning various diseases. While numerous computational methods for predicting PPIS have emerged, they have indeed mitigated the labor and time constraints associated with traditional experimental methods. However, the predictive accuracy of these methods has yet to reach the desired threshold. In this context, we proposed a groundbreaking graph-based computational model called GHGPR-PPIS. This innovative model leveraged a graph convolutional network using heat kernel (GraphHeat) in conjunction with Generalized PageRank techniques (GHGPR) to predict PPIS. Additionally, building upon the GHGPR framework, we devised an edge self-attention feature processing block, further augmenting the performance of the model. Experimental findings conclusively demonstrated that GHGPR-PPIS surpassed all competing state-of-the-art models when evaluated on the benchmark test set. Impressively, on two distinct independent test sets and a specific protein chain, GHGPR-PPIS consistently demonstrated superior generalization performance and practical applicability compared to the comparative model, AGAT-PPIS. Lastly, leveraging the t-SNE dimensionality reduction algorithm and clustering visualization technique, we delved into an interpretability analysis of the effectiveness of GHGPR-PPIS by meticulously comparing the outputs from different stages of the model.

Fusing Sequence and Structural Knowledge by Heterogeneous Models to Accurately and Interpretively Predict Drug-Target Affinity.

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence features of drug SMILES, targets, and pockets and their corresponding graph structural features using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model's performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, such as DeepDTA, GraphDTA, and DeepDTAF, S2DTA's performance became more evident. It exhibited a 25.2% reduction in mean absolute error (MAE) and a 20.1% decrease in root mean square error (RMSE). Additionally, S2DTA showed some improvements in other crucial metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2, with these metrics experiencing increases of 19.6%, 17.5%, 8.1%, and 49.4%, respectively. Finally, we conducted an interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism. The analysis results supported that S2DTA was an effective and accurate tool for predicting DTA.

Impact of digital finance on energy efficiency: empirical findings from China.

The world is facing the problem of resource scarcity and environmental degradation. Improving energy efficiency is an effective way to reduce energy consumption and reduce pollutant emissions. Based on relevant data from 30 Chinese provinces from 2011 to 2019, this paper constructs energy efficiency indicators by establishing a super-efficient three-stage SBM-DEA model. It explores the impact of digital finance on energy efficiency using a systematic generalized moment estimation method and constructs an analytical framework for the impact of digital inclusive finance on energy efficiency from the breadth of coverage, depth of use, and degree of digitization of digital inclusive finance. In addition, this paper examines the differences in the impact of digital inclusive finance on energy efficiency from a sub-regional perspective. Research indicates the following: (1) At the national level, the relationship between digital inclusive finance development and energy efficiency in China shows an inverted "U"-shape; the breadth of digital financial coverage, the use of digital insurance services and digital credit services, and the degree of digitalization of digital finance all have significant effects on energy efficiency. (2) From a regional perspective, the impact of digital inclusive finance on energy efficiency has regional heterogeneity. Based on this finding, first, the government should speed up the construction of digital financial infrastructure to promote the further development of digital finance. Second, the government should take appropriate measures to regulate industry giants. Third, the government should adjust measures to local conditions when formulating policies. The above research has certain implications for improving the targeting of digital finance-related policies and promoting the high-quality development of China's economy.

Relationship between health spending, life expectancy and renewable energy in China: A new evidence from the VECM approach.

There has been a growing trend in health spending and renewable energy consumption in China over the past few decades, which has positive implications for health outcomes, such as life expectancy. Therefore, the main objective of this study is to empirically analyze the impact of health expenditures and renewable energy on life expectancy in China. We used the time series data from 2000Q1 to 2020Q4 and applied the VECM approach for the data analysis. The results of this study suggest a long run association between health spending, life expectancy and renewable energy. The increase in health spending improves life expectancy, while renewable energy consumption also positively affects life expectancy in China. Therefore, the government should allocate sufficient funding to the health sector in order to attain higher life expectancy in the country. In addition, the government should also provide incentives for the consumption and production of renewable energy, which could help to achieve the sustainable development goal and life expectancy.

The impact of public health events on green economy efficiency in the context of environmental regulation.

Public health crises have become one of the greatest threats to sustainable global economic development. It is therefore important to explore the impact of public health events on green economic efficiency. However, few studies have specifically examined the relationship between public health security and green economic efficiency. Based on the relevant data of 30 Chinese provinces from 2011 to 2019, this paper explores the impact of public health on green economic efficiency by establishing a four-stage SBM-DEA model to construct green economic efficiency indicators and using a panel model. A moderating effect model is established to explore the moderating effect of environmental regulation on the impact of public health on green economic efficiency. In addition, this paper examines the heterogeneity of public health impact on green economic efficiency in terms of geographic location, carbon pilot, and transportation level. It is found that, first, public health events have a significant hindering effect on green economic efficiency. Second, environmental regulation has a significant moderating effect on the impact of public health events on green economic efficiency. Third, the impact of public health events on green economic efficiency changes from hindering to facilitating as the intensity of environmental regulation increases. Fourth, the impact of public health events on green economic efficiency is heterogeneous in terms of geographic location, carbon pilot, and transportation level. The above studies have implications for how to balance economic development and environmental protection in case of a public safety event.

The influence of social capital on farmers' green control technology adoption behavior.

Relying on social capital to promote farmers' adoption of green control technology is of great significance for the governance of rural environment and the realization of sustainable agricultural development. Based on the survey data of 754 farmers in Shandong Province, this paper uses the Probit model and the instrumental variable method to empirically analyze the impact of social capital on farmers' green control technology adoption behavior. The results show that: social capital has a promoting influence on farmers' green control technology adoption behavior; the influence of the three dimensions of social capital on farmers' green control technology adoption behavior is in turn social norms, social networks, and social trust; social networks play an enhanced moderating role in the process of social trust and social norms promoting farmers' green control technology adoption behavior; education level, the number of family labor force and annual family income level have a significant positive impact on farmers' green control technology adoption behavior, while age has a significant negative impact. Therefore, the government should make full use of social capital to promote farmers to adopt green control technology.

Driving mechanism of subjective cognition on farmers' adoption behavior of straw returning technology: Evidence from rice and wheat producing provinces in China.

Straw burning is one of the important causes of environmental pollution in rural China. As an important green production technology, straw returning is beneficial to the improvement of rural environment and the sustainable development of agriculture. Based on the improved planned behavior theory, taking the survey data of 788 farmers in Shandong, Henan, Hubei, and Hunan provinces as samples, this paper uses a multi-group structural equation model to explore the driving mechanism of subjective cognition on the adoption behavior of farmers' straw returning technology. The results show that behavioral attitude, subjective norm, and perceived behavioral control, which represent subjective cognition, all have significant driving effects on farmers' intention to adopt straw returning technology. Behavioral intention plays a mediating role in the process of subjective cognition driving farmers' adoption behavior of straw returning technology. Government support has a moderating role in the path from farmers' behavioral intention to behavioral response. The subjective cognition of different types of farmers has a significant driving effect on the adoption intention of straw returning technology, but the driving strength weakens with the increase of the degree of farmers' concurrent occupation. This study provides guidance for improving the government's straw returning policy and regulating straw returning behavior.

Agricultural Openness and the Risk of COVID-19 Incidence: Evidence from China.

At present, there are large number of articles on the impact of COVID-19, but there are only a few articles on the impact of COVID-19 and international agriculture. Agriculture product is different from other industrial products. If domestic food cannot be self-sufficient, it must be resolved through imports. This will inevitably face the dilemma between the opening up agriculture and the risk of importing COVID-19. This paper pioneered the use of entropy method, TOPSIS method and grey correlation analysis to predict the correlation between agricultural opening to the outside world and the input and spread of COVID-19. We use the correlation matrix quantifying the number of confirmed COVID-19 cases and agricultural openness to deduce that there is a significant positive correlation between the flow of agricultural products caused by China's agricultural opening-up and the spread of COVID-19, and use the proposed matrix to predict the spread risk of COVID-19 in China. The results of the empirical analysis can provide strong evidence for decision-makers to balance the risk of COVID-19 transmission with the opening of agricultural markets, and they can take this evidence into full consideration to formulate reasonable policies. This has great implications both for preventing the spread of COVID-19 and for agricultural opening-up.

Digital Economy Development, Industrial Structure Upgrading and Green Total Factor Productivity: Empirical Evidence from China's Cities.

The digital economy is an important engine to promote sustainable economic growth. Exploring the mechanism by which the digital economy promotes economic development, industrial upgrading and environmental improvement is an issue worth studying. This paper takes China as an example for study and uses the data of 286 cities from 2011 to 2019. In the empirical analysis, the direction distance function (DDF) and the Global Malmquist-Luenberger (GML) productivity index methods are used to measure the green total factor productivity (GTFP), while Tobit, quantile regression, impulse response function and intermediary effect models are used to study the relationship among digital economy development, industrial structure upgrading and GTFP. The results show that: (1) The digital economy can significantly improve China's GTFP; however, there are clear regional differences. (2) The higher the GTFP, the greater the promotion effect of the digital economy on the city's GTFP. (3) From a dynamic long-term perspective, the digital economy has indeed positively promoted China's GTFP. (4) The upgrading of industrial structures is an intermediary transmission mechanism for the digital economy to promote GTFP. This paper provides a good reference for driving green economic growth and promoting the environment.

Does Environmental Regulation Promote Industrial Green Technology Progress? Empirical Evidence from China with a Heterogeneity Analysis.

The complex relationship between environmental regulation and green technology progress has always been a hot topic of research, especially in developing countries, where the impact of environmental regulation is important. Current research is mainly concerned with the impact of the single environmental regulation on technological progress and lacks study on the diversity of environmental regulations. The main purpose of this paper is to examine the heterogeneity of the effects of different types of environmental regulation on industrial green technology progress. As China's scale of economy and pollution emissions are both large, and the government has also made great efforts in environmental regulation, this paper takes China as the example for analyses. We first use the EBM-GML method to measure the industrial green technology progress of 30 provinces in China from 2000 to 2018, and then apply the panel econometric model and threshold model to empirically investigate the influence of 3 types of environmental regulation. The results show that, first, the impacts of environmental regulation on industrial green technology progress are significantly different; specifically, command-based regulation has no direct significant impact, and autonomous regulation has played a positive role, and market-based regulation's quadratic curve effect is significant, in which the cost-based and investment-based tool presents an inverted U-sharped and U-sharped, respectively. Second, there may be a weak alternative interaction among different types of environmental regulation. Third, a market-based regulatory tool has a threshold effect; with the upgrading of environmental regulation compliance, the effect of a cost-based tool is characterized by "promotion inhibition", and that of an investment-based tool is "inhibition promotion". Finally, the results of regional analysis are basically consistent with those of the national analysis. Based on the study, policy enlightenment is put forward to improve regional industrial green technology progress from the perspective of environmental regulation. This paper can provide a useful analytical framework for studying the relationship between environmental regulation and technological progress in a country, especially in developing countries.

Does the digital finance revolution validate the Environmental Kuznets Curve? Empirical findings from China.

In recent years, digital finance has become a crucial part of the financial system and reshaped the mode of green finance in China. Digital finance has brought certain impact on economic growth, industrial structure, and resident income, which may affect pollution. The nexus of digital finance and environment in China is thus worth exploring. By revising the traditional Environmental Kuznets Curve model with income inequality variable, this paper decomposes the environmental effects of economic activities into income growth effect, industrial structure effect and income inequality effect, and use panel data of China's provinces to conduct an empirical analysis. The results reveal the following: (1) the Environmental Kuznets Curve is still valid in sample, and digital finance can reduce air and water pollution (as measured through SO2 and COD emission) directly; (2) in the influence mechanism, digital finance can alleviate income inequality and promote green industrial structure, thus reducing pollution indirectly, but the scale effect of income growth outweighs the technological effect, which increases pollution indirectly; and (3) digital finance has a threshold effect on improving the environment, then an acceleration effect appears after a certain threshold value. From the regional perspective, digital finance development in eastern regions is generally ahead of central and western regions, and the effects of environmental improvement in the eastern regions are greater. According to the study, this paper suggest that digital finance can be an effective way to promote social sustainability by alleviating income inequality and environmental sustainability by reducing pollution.