This paper aims to explore the effect of teacher–student collaboration on academic innovation in universities in different stages of collaboration.
Abstract
Purpose
This paper aims to explore the effect of teacher–student collaboration on academic innovation in universities in different stages of collaboration.
Design/methodology/approach
Based on collaboration life cycle, this paper divided teacher–student collaboration into initial, growth and mature stages to explore how teacher–student collaboration affects academic innovation.
Findings
Collecting data from National Science Foundation of China, the empirical analysis found that collaboration increases the publication of local (Chinese) papers at all stages. However, teacher–student collaboration did not significantly improve the publication of international (English) papers in the initial stage. In the growth stage, teacher–student collaboration has a U-shaped effect on publishing English papers, while its relationship is positive in the mature stage.
Practical implications
The results offer suggestions for teachers and students to choose suitable partners and also provide some implications for improving academic innovation.
Originality/value
This paper constructed a model in which the effect of teacher–student collaboration on academic innovation in universities was established.
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Keywords
Huaxiang Song, Hanjun Xia, Wenhui Wang, Yang Zhou, Wanbo Liu, Qun Liu and Jinling Liu
Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy…
Abstract
Purpose
Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.
Design/methodology/approach
To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.
Findings
We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.
Originality/value
This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.
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Abstract
Purpose
Mega construction projects (MCPs), which play an important role in the economy, society and environment of a country, have developed rapidly in recent years. However, due to frequent social conflicts caused by the negative social impact of MCPs, social risk control has become a major challenge. Exploring the relationship between social risk factors and social risk from the perspective of risk evolution and identifying key factors contribute to social risk control; but few studies have paid enough attention to this. Therefore, this study aims to systematically analyze the impact of social risk factors on social risk based on a social risk evolution path.
Design/methodology/approach
This study proposed a social risk evolution path for MCPs explaining how social risk occurs and develops with the impact of social risk factors. To further analyze the impact quantitatively, a social risk analysis model combining structural equation model (SEM) with Bayesian network (BN) was developed. SEM was used to verify the relationship in the social risk evolution path. BN was applied to identify key social risk factors and predict the probabilities of social risk, quantitatively. The feasibility of the proposed model was verified by the case of water conservancy projects.
Findings
The results show that negative impact on residents’ living standards, public opinion advantage and emergency management ability were key social risk factors through sensitivity analysis. Then, scenario analysis simulated the risk probability results with the impact of different states of these key factors to obtain management strategies.
Originality/value
This study creatively proposes a social risk evolution path describing the dynamic interaction of the social risk and first applies the hybrid SEM–BN method in the social risk analysis for MCPs to explore effective risk control strategies. This study can facilitate the understanding of social risk from the perspective of risk evolution and provide decision-making support for the government coping with social risk in the implementation of MCPs.
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Wanyi Chen and Fanli Meng
Unpredictable economic landscapes have led to a continuous escalation in global economic policy uncertainty (EPU). Improving risk management and sustainability in an environment…
Abstract
Purpose
Unpredictable economic landscapes have led to a continuous escalation in global economic policy uncertainty (EPU). Improving risk management and sustainability in an environment with high macro risk is critical for business development. This study aims to explore the impact of corporate sustainable development on corporate tax risk.
Design/methodology/approach
After using a sample of companies that were A-share listed on the Shanghai and Shenzhen stock exchanges from 2011 to 2021, this paper applies ordinary least squares and a moderate effect model.
Findings
Better environmental, social and governance (ESG) performance can weaken corporate tax risk by improving green innovation capability, reputation and information transparency. Meanwhile, the restraining effect of ESG on tax risk was more significant amid high EPU. These impacts were amplified amid higher market competition, lower tax supervision and a lower degree of corporate digital transformation.
Practical implications
The findings emphasize the need for the government to establish a healthy business and tax environment so that enterprises can improve sustainable development and increase their risk management abilities, especially post-COVID-19.
Social implications
This study guides enterprises and the entirety of society to in paying attention to and promoting ESG practices, which can enhance enterprise tax management.
Originality/value
This study expands the research on the economic consequences of sustainable development and the factors influencing corporate tax risk and EPU.