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1 – 8 of 8Ting Zhou, Yingjie Wei, Jian Niu and Yuxin Jie
Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a…
Abstract
Purpose
Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).
Design/methodology/approach
The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.
Findings
The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.
Originality/value
This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.
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Keywords
Yaqiao Liu, Yifei Liang and Yilan Guo
The marketisation of higher education fosters the notion of students as consumers, highlighting the shifting dynamics of student–teacher relationships. This paper aims to…
Abstract
Purpose
The marketisation of higher education fosters the notion of students as consumers, highlighting the shifting dynamics of student–teacher relationships. This paper aims to contribute to ongoing discussions about students as consumers and their involvement in pedagogical practices. We explore students’ experiences in short-term study abroad (SA) programmes that involve collaborative learning, examining how a consumerism-oriented approach affects students’ perceptions of their pedagogical identities and student–teacher pedagogical relationships.
Design/methodology/approach
A qualitative exploratory study was conducted to capture students’ rich and subjective perceptions and experiences. The data were gathered through semi-structured interviews with 15 Chinese undergraduate students who participated in a short-term SA programme at a UK university. Following data translation and transcription, a thematic analysis approach facilitated our exploration.
Findings
Chinese students engage in SA programmes as a strategic investment in personal growth and transformation, with their consumer-oriented identity fostering a mutually beneficial relationship with educators and group members. This consumer mindset appears to enhance active student engagement and, to some extent, create reciprocal student–teacher interactions through power sharing and collaborative involvement.
Originality/value
This study presents empirical data exploring the impact of consumer identity on the dynamics of student–teacher relationships in the SA context. It provides recommendations for implementing pedagogical approaches designed to mediate the influence of consumerism on student engagement, particularly in shaping collaborative student–teacher relationships. This study offers insights for future research on the effects of consumerism in higher education within cross-cultural contexts.
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Cong Cao, Chengxiang Chu, Xinyi Ding and Yangyan Shi
As live streaming becomes a widely used online sales mode, previously content-centred anchors are attempting to switch to e-commerce live streaming. The purpose of this research…
Abstract
Purpose
As live streaming becomes a widely used online sales mode, previously content-centred anchors are attempting to switch to e-commerce live streaming. The purpose of this research was to explore the mechanisms that prompt consumers to stay or leave after content anchors transfer to live e-commerce broadcasts. In addition, we explored the factors affecting consumption from the perspectives of anchors, consumers and the external environment.
Design/methodology/approach
We distributed questionnaires to a group of fans who had experienced the transition of content anchors to live streaming and received back 375 valid questionnaires. Using psychological contract theory, we constructed a theoretical model for the scenario in which content anchors transition to live e-commerce broadcasting and analysed the data using partial least squares structural equation modelling (PLS-SEM).
Findings
The results show that circle culture, mainstream culture, initial trust and live streaming content all positively influenced consumers’ attitudes, whilst consumers’ past shopping experiences negatively influenced consumers’ attitudes. The personal charm of the content anchors did not have a significant effect on consumers’ attitudes. Additionally, we found that only anchors with a significant circle culture and good trust levels amongst fans were able to transition to live e-commerce streaming successfully.
Originality/value
This study extends the application of psychological contract theory to the field of e-commerce and describes the transformation of different types of psychological contracts. The paper’s conclusions provide a reference for decision-making and the implementation of transformation by content-based anchors to live streaming, helping them to coordinate their relationships with fans more effectively.
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Li Li, Tong Huang, Chujia Pan, J.F. Pan and Wenbin Su
The purpose of this paper aims to investigate the adaptive impedance control and its optimized PSO algorithm for force tracking of a dual-arm cooperative robot. Because the…
Abstract
Purpose
The purpose of this paper aims to investigate the adaptive impedance control and its optimized PSO algorithm for force tracking of a dual-arm cooperative robot. Because the dual-arm robot is directly in contact with external environment, controlling the mutual force between robot and external environment is of great importance. Besides, a high compliance of the robot should be guaranteed.
Design/methodology/approach
An impedance control based on Particle Swarm Optimization (PSO) algorithm is designed to track the mutual force and achieve compliance control of the robot end.
Findings
The experimental results show that the impedance control coefficients can be automatically tuned converged by PSO algorithm.
Originality/value
The system can reach a steady state within 0.03 s with overshoot convergence, and the force fluctuation range at the steady state decreases to about ±0.08 N even under the force mutation condition.
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WenFeng Qin, Yunsheng Xue, Hao Peng, Gang Li, Wang Chen, Xin Zhao, Jie Pang and Bin Zhou
The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation…
Abstract
Purpose
The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.
Design/methodology/approach
A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.
Findings
The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.
Originality/value
The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.
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Teresina Torre, Damiano Petrolo, Massimiliano Matteo Pellegrini and Daria Sarti
The study aims to deepen existing knowledge on the specific role of soft total quality management (TQM) practices in the ferry sector. Over the last decade, TQM practices have…
Abstract
Purpose
The study aims to deepen existing knowledge on the specific role of soft total quality management (TQM) practices in the ferry sector. Over the last decade, TQM practices have been thoroughly restructured, allowing us to develop an appropriate framework through which the relevance of each practice to this particular sector can be explained.
Design/methodology/approach
A narrative case study has been conducted to enhance the quality orientation and soft TQM practices adopted by a medium-sized company in the ferry sector.
Findings
The study identifies five soft TQM practices that offer valuable contributions in terms of quality orientation. These are organised into a configurational and systemic approach according to a three-level framework. At the macro level, a customer-orientated approach is paramount, as this orientation clearly points out the fundamental values of TQM. Coherently, at the micro-level, employees should be trained, involved, and empowered to truly internalise and behave according to a quality orientation. At the meso-level, dedicated leadership should support these practices and foster their effectiveness across the organisational structure.
Research limitations/implications
The main limitation of this study is related to its narrative analysis. More empirically-grounded research should be used in the future to test the validity of the model.
Practical implications
TQM practices can leverage soft aspects, finding mutual integrations and offering reciprocal support if a bundle of practices is enforced and co-present across several layers of an organisational structure.
Originality/value
The model offers a configurational approach to help the ferry sector in leveraging soft TQM practices to implement TQM initiatives successfully. This is subject to external contingencies and thus requires adaptability and flexibility.
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Ning Xu, Di Zhang, Yutong Li and Yingjie Bai
Green technology innovation is the organic combination of green development and innovation driven. It is also a powerful guarantee for shaping sustainable competitive advantages…
Abstract
Purpose
Green technology innovation is the organic combination of green development and innovation driven. It is also a powerful guarantee for shaping sustainable competitive advantages of manufacturing enterprises. To explore what kind of executive incentive contracts can truly stimulate green technology innovation, this study aims to distinguish the equity incentive and reputation incentive, upon their contractual elements characteristics and green governance effects, and then put forward suggestions for green technology innovation accordingly.
Design/methodology/approach
This study establishes an evaluation model and uses empirical methods to test. Concretely, using data from A-share listed manufacturing companies for the period from 2007 to 2020, this study compares and analyzes the impact of equity and reputation incentive on green technology innovation and explores the relationship between internal green business behavior and external green in depth.
Findings
This study finds that reputation incentives focus on long-term and non-utilitarian orientation, which can promote green technology innovation in enterprises. While equity incentives, linked to performance indicators, have a inhibitory effect on green technology innovation. Internal and external institutional factors such as energy conservation measures, the “three wastes” management system, and environmental recognition play the regulatory role in the relationship between incentive contracts and green technology innovation.
Originality/value
Those findings validate and expand the efficient contracting hypothesis and the rent extraction hypothesis from the perspective of green technology innovation and provide useful implications for the design of green governance systems in manufacturing enterprises.
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Ambica Ghai, Pradeep Kumar and Samrat Gupta
Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered…
Abstract
Purpose
Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.
Design/methodology/approach
The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.
Findings
The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.
Research limitations/implications
This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.
Practical implications
This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.
Social implications
In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.
Originality/value
This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.
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