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1 – 10 of 177Ziyuan Ma, Huajun Gong and Xinhua Wang
The purpose of this paper is to construct an event-triggered finite-time fault-tolerant formation tracking controller, which can achieve a time-varying formation control for…
Abstract
Purpose
The purpose of this paper is to construct an event-triggered finite-time fault-tolerant formation tracking controller, which can achieve a time-varying formation control for multiple unmanned aerial vehicles (UAVs) during actuator failures and external perturbations.
Design/methodology/approach
First, this study developed the formation tracking protocol for each follower using UAV formation members, defining the tracking inaccuracy of the UAV followers’ location. Subsequently, this study designed the multilayer event-triggered controller based on the backstepping method framework within finite time. Then, considering the actuator failures, and added self-adaptive thought for fault-tolerant control within finite time, the event-triggered closed-loop system is subsequently shown to be a finite-time stable system. Furthermore, the Zeno behavior is analyzed to prevent infinite triggering instances within a finite time. Finally, simulations are conducted with external disturbances and actuator failure conditions to demonstrate formation tracking controller performance.
Findings
It achieves improved performance in the presence of external disturbances and system failures. Combining limited-time adaptive control and event triggering improves system stability, increase robustness to disturbances and calculation efficiency. In addition, the designed formation tracking controller can effectively control the time-varying formation of the leader and followers to complete the task, and by adding a fixed-time observer, it can effectively compensate for external disturbances and improve formation control accuracy.
Originality/value
A formation-following controller is designed, which can handle both external disturbances and internal actuator failures during formation flight, and the proposed method can be applied to a variety of formation control scenarios and does not rely on a specific type of UAV or communication network.
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Rongxin Chen and Tianxing Zhang
In the global context, artificial intelligence (AI) technology and environmental, social and governance (ESG) have emerged as central drivers facilitating corporate transformation…
Abstract
Purpose
In the global context, artificial intelligence (AI) technology and environmental, social and governance (ESG) have emerged as central drivers facilitating corporate transformation and the business model revolution. This paper aims to investigate whether and how the application of AI enhances the ESG performance of enterprises.
Design/methodology/approach
This study uses panel data from Chinese A-share listed companies spanning the period from 2012 to 2022. Through a multivariate regression analysis, it examines the impact of AI on the ESG performance of enterprises.
Findings
The findings suggest that the application of AI in enterprises has a positive impact on ESG performance. Internal control systems within the organization and external information environments act as mediators in the relationship between AI and corporate ESG performance. Furthermore, corporate compliance plays a moderating role in the connection between AI and corporate ESG performance.
Originality/value
This paper underscores the pivotal role played by AI in enhancing corporate ESG performance. It explores the pathways to improving corporate ESG behavior from the perspectives of internal control and information environments. This discussion holds significant implications for advancing the application of AI in enterprises and enhancing their sustainable governance capabilities.
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Zhiyu Dong, Ruize Qin, Ping Zou, Xin Yao, Peng Cui, Fan Zhang and Yizhou Yang
The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation…
Abstract
Purpose
The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.
Design/methodology/approach
The DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.
Findings
The results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.
Originality/value
Based on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.
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Meng Zhu and Xiaolong Xu
Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is…
Abstract
Purpose
Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.
Design/methodology/approach
ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.
Findings
We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.
Originality/value
This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.
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Xuliang Yao, Xiao Han, Yuefeng Liao and Jingfang Wang
This study aims to solve the problem that under light-load conditions, the output voltage regulation capability is lost due to the fact that the voltage gain of the LLC resonant…
Abstract
Purpose
This study aims to solve the problem that under light-load conditions, the output voltage regulation capability is lost due to the fact that the voltage gain of the LLC resonant converter does not decrease with the increase of the switching frequency.
Design/methodology/approach
In this paper, the impedance model considering the parasitic parameters of the primary and secondary sides is calculated under light-load conditions, the limitations of the previous method are explained and a new circuit improvement is proposed.
Findings
In this paper, an improved circuit is proposed, and the impedance Bode plot is used to verify that the circuit can effectively improve the voltage gain problem under light-load conditions. Finally, the experimental results verify the effectiveness of the proposed circuit through comparison with traditional solutions and circuits.
Originality/value
In this paper, the impedance model considering the parasitic parameters of the primary and secondary sides is calculated, the limitations of the previous method are explained and a new circuit improvement is proposed. When compared with the previous method, the proposed circuit improvement can suppress the voltage gain increase that occurs when the switching frequency increases to a certain level.
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Muhammad Waqas, Dingyong He, Zhen Tan, Peng Yang, Mu Gao and Xingye Guo
The selective laser melting (SLM) technique, as a typical additive manufacturing process, is widely used for the fabrication of metallic biomedical components. In terms of…
Abstract
Purpose
The selective laser melting (SLM) technique, as a typical additive manufacturing process, is widely used for the fabrication of metallic biomedical components. In terms of biodegradability, zinc and its alloys represent an emerging generation of metallic materials for biomedical implants. The purpose of this paper is to obtain the Zn and Zn10Mg alloys with high mechanical properties using the SLM technology. The relationship between the processing parameters and the porosity of pure Zn and Zn10Mg alloy samples was investigated.
Design/methodology/approach
The samples were fabricated using SLM technology working in an inert gas closed chamber. Preliminary experiments were conducted to analyze the laser power and gas flow on evaporation, single track form and porosity. To evaluate the influence of factors on relative density, the response surface methodology was applied.
Findings
The satisfactory results of the proposed method were achieved, in which the relative density of the components reached up to 99.63%, and compression strength reached 214 ± 13 MPa under optimal processing conditions.
Originality/value
Zinc is categorized by its low melting and boiling point, which leads to the high porosity of the components. It is difficult to prepare the Zn alloy samples with high relative density using SLM technology. This work successfully achieved dense Zn and Zn10Mg samples and investigated their microstructure, mechanical properties and corrosion behavior.
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Xuliang Yao, Xiao Han, Yuefeng Liao and Jingfang Wang
This paper aims to better design the resonant tank parameters for LLC resonant converter. And, it is found that under heavy load, the voltage gain is affected by junction…
Abstract
Purpose
This paper aims to better design the resonant tank parameters for LLC resonant converter. And, it is found that under heavy load, the voltage gain is affected by junction capacitors of the primary side switching and the parasitic parameters of the secondary side diodes converted to the primary side, which will cause the voltage gain decreased when the switching frequency decreased.
Design/methodology/approach
This paper proposes an optimization parameters design method to solve this problem, which was based on impedance model considering the parasitic parameters of switching devices and diodes.
Findings
The effectiveness of the proposed method is verified by impedance Bode plots and experimental results.
Originality/value
From the perspective of impedance modeling, this paper finds the reasons for the insufficient voltage regulation capability of LLC resonant converters under heavy load and finds solutions through analysis.
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Sakshi Yadav, Shivendra Kumar Pandey and Dheeraj Sharma
This study aims to answer two significant questions: What are the relative consumer and firm-level effects of marketing through metaverse compared to conventional marketing…
Abstract
Purpose
This study aims to answer two significant questions: What are the relative consumer and firm-level effects of marketing through metaverse compared to conventional marketing endeavours? What are the current trends in utilizing the metaverse as reported in the recent literature?
Design/methodology/approach
This study uses a systematic literature review methodology, using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart to synthesize existing research. A total of 35 articles written in English were selected and analysed from two databases, Web of Science and EBSCO Host.
Findings
The findings indicate that consumer-level effects of the metaverse include consumer loyalty and brand attachment. The firm-level benefits are decentralization and cost reductions. The paper proposes a framework indicating variables that could attenuate or enhance the association between immersive components of the metaverse and their resultant effects.
Originality/value
This study contributes to understanding the role of metaverse in marketing practices related to the marketing mix components. The study conceptualizes a novel framework for the metaverse and its resultant effects.
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Yan Li, Ming K. Lim, Weiqing Xiong, Xingjun Huang, Yuhe Shi and Songyi Wang
Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental…
Abstract
Purpose
Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.
Design/methodology/approach
This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.
Findings
The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.
Research limitations/implications
The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.
Originality/value
In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.
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Rania Ahmed Aly El Garem, Amira Fouad and Hassan Mohamed
This paper explores the effect of perceived service quality, trust, perceived value and perceived cost on patient satisfaction and loyalty as well as exploring the moderating…
Abstract
Purpose
This paper explores the effect of perceived service quality, trust, perceived value and perceived cost on patient satisfaction and loyalty as well as exploring the moderating role of the sociodemographic factors.
Design/methodology/approach
The data were gathered from 462 patients via a structured questionnaire, while structural equation modeling was utilized for the analysis.
Findings
Results indicated that trust, perceived value and patient satisfaction have important roles in shaping the patient loyalty, while patient satisfaction was found to fully mediate the patient’s perceived service quality. Loyalty relationship was also found to partially mediate the trust–loyalty relationship. Nonetheless, the patient’s satisfaction–loyalty relationship was found to be only moderated by the age factor.
Practical implications
Implications are provided to the Egyptian private hospitals in order for them to formulate improvement plans as well as set higher standards of conduct.
Originality/value
This original research is the first one, up to the researcher knowledge, that explores the drivers of patient satisfaction in the private hospitals in Egypt.
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