Zhongcheng Gui, Yongjun Deng, Zhongxi Sheng, Tangjie Xiao, Yonglong Li, Fan Zhang, Na Dong and Jiandong Wu
This paper aims to present a new intelligent wall-climbing welding robot system for large-scale steel structure manufacture, which is composed of robot body, control system and…
Abstract
Purpose
This paper aims to present a new intelligent wall-climbing welding robot system for large-scale steel structure manufacture, which is composed of robot body, control system and welding system.
Design/methodology/approach
The authors design the robot system according to application requirements, validate the design through simulation and experiments and use the robot in actual production.
Findings
Experimental results show that the robot system satisfies the demands of automatic welding of large-scale ferromagnetic structure, which contributes much to on-site manufacturing of such structures.
Practical implications
The robot can work with better quality and efficiency compared with manual welding and other semi-automatic welding devices, which can much improve large-scale steel structure manufacturing.
Originality/value
The robot system is a novel solution for large-scale steel structures welding. There are three major advantages: the robot body with reliable adsorption ability, large payload capability and good mobility which meet the requirements of welding; the control system with good welding seam tracking accuracy and intelligent automatic welding ability; and friendly human – computer interface which makes the robot easy to use.
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Jiandong Chen, Yinyin Wu, Chong Xu, Malin Song and Xin Liu
Non-fossil fuels are receiving increasing attention within the context of addressing global climate challenges. Based on a review of non-fossil fuel consumption in major countries…
Abstract
Purpose
Non-fossil fuels are receiving increasing attention within the context of addressing global climate challenges. Based on a review of non-fossil fuel consumption in major countries worldwide from 1985 to 2015, the purpose of this paper is to analyze trends for global non-fossil fuel consumption, share of fuel consumption and inequality.
Design/methodology/approach
The similarities were obtained between the logarithmic mean divisia index and the mean-rate-of-change index decomposition analysis methods, and a method was proposed for complete decomposition of the incremental Gini coefficient.
Findings
Empirical analysis showed that: global non-fossil fuel consumption accounts for a small share of the total energy consumption, but presents an increasing trend; the level of global non-fossil fuel consumption inequality is high but has gradually declined, which is mainly attributed to the concentration effect; inequality in global non-fossil fuel consumption is mainly due to the difference between nuclear power and hydropower consumption, but the contributions of nuclear power and hydropower to per capita non-fossil fuel consumption are declining; and population has the greatest influence on global non-fossil fuel consumption during the sampling period.
Originality/value
The main contribution of this study is its analysis of global non-fossil fuel consumption trends, disparities and driving factors. In addition, a general formula for complete index decomposition is proposed and the incremental Gini coefficient is wholly decomposed.
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Xiancheng Ou, Yuting Chen, Siwei Zhou and Jiandong Shi
With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the…
Abstract
Purpose
With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.
Design/methodology/approach
The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.
Findings
Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.
Research limitations/implications
A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.
Originality/value
In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.
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Jiandong Lu, Xiaolei Wang, Liguo Fei, Guo Chen and Yuqiang Feng
During the coronavirus disease 2019 (COVID-19) pandemic, ubiquitous social media has become a primary channel for information dissemination, social interactions and recreational…
Abstract
Purpose
During the coronavirus disease 2019 (COVID-19) pandemic, ubiquitous social media has become a primary channel for information dissemination, social interactions and recreational activities. However, it remains unclear how social media usage influences nonpharmaceutical preventive behavior of individuals in response to the pandemic. This paper aims to explore the impacts of social media on COVID-19 preventive behaviors based on the theoretical lens of empowerment.
Design/methodology/approach
In this paper, survey data has been collected from 739 social media users in China to conduct structural equation modeling (SEM) analysis.
Findings
The results indicate that social media empowers individuals in terms of knowledge seeking, knowledge sharing, socializing and entertainment to promote preventive behaviors at the individual level by increasing each person's perception of collective efficacy and social cohesion. Meanwhile, social cohesion negatively impacts the relationship between collective efficacy and individual preventive behavior.
Originality/value
This study provides insights regarding the role of social media in crisis response and examines the role of collective beliefs in the influencing mechanism of social media. The results presented herein can be used to guide government agencies seeking to control the COVID-19 pandemic.
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Cheng Lei, Haiyang Mao, Yudong Yang, Wen Ou, Chenyang Xue, Zong Yao, Anjie Ming, Weibing Wang, Ling Wang, Jiandong Hu and Jijun Xiong
Thermopile infrared (IR) detectors are one of the most important IR devices. Considering that the surface area of conventional four-end-beam (FEB)-based thermopile devices cannot…
Abstract
Purpose
Thermopile infrared (IR) detectors are one of the most important IR devices. Considering that the surface area of conventional four-end-beam (FEB)-based thermopile devices cannot be effectively used and the performance of this type of devices is relatively low, this paper aims to present a double-end-beam (DEB)-based thermopile device with high duty cycle and performance. The paper aims to discuss these issues.
Design/methodology/approach
Numerical analysis was conducted to show the advantages of the DEB-based thermopile devices.
Findings
Structural size of the DEB-based thermopiles may be further scaled down and maintain relatively higher responsivity and detectivity when compared with the FEB-based thermopiles. The authors characterized the thermoelectric properties of the device proposed in this paper, which achieves a responsivity of 1,151.14 V/W, a detectivity of 4.15 × 108 cm Hz1/2/W and a response time of 14.46 ms sensor based on DEB structure.
Orginality/value
The paper proposed a micro electro mechanical systems (MEMS) thermopile infrared sensor based on double-end-beam structure.
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Jiandong Zhou, Xiang Li, Xiande Zhao and Liang Wang
The purpose of this paper is to deal with the practical challenge faced by modern logistics enterprises to accurately evaluate driving performance with high computational…
Abstract
Purpose
The purpose of this paper is to deal with the practical challenge faced by modern logistics enterprises to accurately evaluate driving performance with high computational efficiency under the disturbance of road smoothness and to identify significantly associated performance influence factors.
Design/methodology/approach
The authors cooperate with a logistics server (G7) and establish a driving grading system by constructing real-time inertial navigation data-enabled indicators for both driving behaviour (times of aggressive speed change and times of lane change) and road smoothness (average speed and average vibration times of the vehicle body).
Findings
The developed driving grading system demonstrates highly accurate evaluations in practical use. Data analytics on the constructed indicators prove the significances of both driving behaviour heterogeneity and the road smoothness effect on objective driving grading. The methodologies are validated with real-life tests on different types of vehicles, and are confirmed to be quite effective in practical tests with 95% accuracy according to prior benchmarks. Data analytics based on the grading system validate the hypotheses of the driving fatigue effect, daily traffic periods impact and transition effect. In addition, the authors empirically distinguish the impact strength of external factors (driving time, rainfall and humidity, wind speed, and air quality) on driving performance.
Practical implications
This study has good potential for providing objective driving grading as required by the modern logistics industry to improve transparent management efficiency with real-time vehicle data.
Originality/value
This study contributes to the existing research by comprehensively measuring both road smoothness and driving performance in the driving grading system in the modern logistics industry.
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Tiebing Shi, Jiandong Li and Chi Lo Lim
This study aims to investigate factors impacting host country consumers’ attitudes toward acquirers’ corporate brands and target brands after cross-border acquisitions (CBAs).
Abstract
Purpose
This study aims to investigate factors impacting host country consumers’ attitudes toward acquirers’ corporate brands and target brands after cross-border acquisitions (CBAs).
Design/methodology/approach
Surveys were conducted with US consumers using two fictitious CBA scenarios in the automobile industry.
Findings
Consumer ethnocentric tendencies (CETs) are negatively related to attitudes toward a CBA event; attitudes toward a CBA event are positively related to post-CBA attitudes toward the acquirer's corporate brand; brand-image fit is positively related to attitudes toward a CBA event, and post-CBA attitudes toward the acquirer's corporate brand and the target brand; post-CBA attitudes toward the acquirer's corporate brand and the target brand are positively related.
Research limitations/implications
This study is limited in the sample, analysis approaches, context and factors examined. Future research could use more representative samples and both quantitative and qualitative methodologies; conduct more tests; examine real CBAs in different industries and countries; and investigate effects of other factors affecting attitudes toward the CBA event and post-CBA brand attitudes.
Practical implications
Managers should consider CETs and brand-image fit and strategically influence attitudes toward a CBA event and post-CBA brand attitudes.
Originality/value
It investigates the mediating effect of attitudes toward a CBA event on the relationship between CETs and post-CBA attitudes toward the acquirer's corporate brand and the effects of brand-image fit on attitudes toward a CBA event and post-CBA brand attitudes.
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Danni Chen, JianDong Zhao, Peng Huang, Xiongna Deng and Tingting Lu
Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The…
Abstract
Purpose
Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability.
Design/methodology/approach
To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems.
Findings
First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated.
Originality/value
An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.
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Sibnath Deb, Esben Strodl and Jiandong Sun
The purpose of this paper is to examine the prevalence of academic stress and exam anxiety among private secondary school students in India as well as the associations with…
Abstract
Purpose
The purpose of this paper is to examine the prevalence of academic stress and exam anxiety among private secondary school students in India as well as the associations with socio-economic and study-related factors.
Design/methodology/approach
Participants were 400 adolescent students (52 percent male) from five private secondary schools in Kolkata who were studying in grades 10 and 12. Participants were selected using a multi-stage sampling technique and were assessed using a study-specific questionnaire.
Findings
Findings revealed that 35 and 37 percent reported high or very high levels of academic stress and exam anxiety respectively. All students reported high levels of academic stress, but those who had lower grades reported higher levels of stress than those with higher grades. Students who engaged in extra-curricula activities were more likely to report exam anxiety than those who did not engage in extra-curricula activities.
Practical implications
Private high school students in India report high levels of academic stress and exam anxiety. As such there is a need to develop effective interventions to help these students better manage their stress and anxiety.
Originality/value
This is the first study the authors are aware of that explores the academic stress levels of private secondary school students in India. The study identifies factors that may be associated with the experience of high levels of stress that need to be explored further in future research.
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Sabri Burak Arzova and Bertaç Şakir Şahin
The purposes of this study are to contribute to the limited green growth (GG) literature in emerging markets, to analyze GG from a financial economy perspective and to determine…
Abstract
Purpose
The purposes of this study are to contribute to the limited green growth (GG) literature in emerging markets, to analyze GG from a financial economy perspective and to determine the contribution of financial development and innovation to GG in Brazil, Russian Federation, India, China and South Africa and Türkiye (BRICS-T). BRICS-T countries significantly impact the world population, international politics, energy resources and economy. In addition, BRICS-T countries are one of the leading countries in the world with their sustainability efforts. Investigating the GG model in these countries may contribute to structuring emerging economies around the principles of GG and advancing global green transformation efforts.
Design/methodology/approach
The authors applied panel data analysis from 2001 to 2019. GG is economic growth free from environmental depletion in the model. National income, personnel expenditure and foreign direct investments are macroeconomic variables. These variables measure economic development and promote economic and social progress, which is essential for GG. Capital accumulation and innovation are essential tools in GG transformation. Therefore, financial development and patent applications represent the moderating variables. The authors estimate the fixed effect model with Parks-Kmenta robust.
Findings
Empirical results show that national income growth and foreign direct investments positively affect GG. Personnel expenditure negatively affects GG. On the contrary, financial development and patent growth have little moderating role.
Originality/value
This study contributes to the literature on creating a GG model in emerging countries. The study is original in its model and sample.