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1 – 10 of 14Boussad Moualek, Simon Chauviere, Lamia Belguerras, Smail Mezani and Thierry Lubin
The purpose of this study is to develop a magnetic resonance imaging (MRI)-safe iron-free electrical actuator for MR-guided surgical interventions.
Abstract
Purpose
The purpose of this study is to develop a magnetic resonance imaging (MRI)-safe iron-free electrical actuator for MR-guided surgical interventions.
Design/methodology/approach
The paper deals with the design of an MRI compatible electrical actuator. Three-dimensional electromagnetic and thermal analytical models have been developed to design the actuator. These models have been validated through 3D finite element (FE) computations. The analytical models have been inserted in an optimization procedure that uses genetic algorithms to find the optimal parameters of the actuator.
Findings
The analytical models are very fast and precise compared to the FE models. The computation time is 0.1 s for the electromagnetic analytical model and 3 min for the FE one. The optimized actuator does not perturb imaging sequence even if supplied with a current 10 times higher than its rated one. Indeed, the actuator’s magnetic field generated in the imaging area does not exceed 1 ppm of the B0 field generated by the MRI scanner. The actuator can perform up to 25 biopsy cycles without any risk to the actuator or the patient since he maximum temperature rise of the actuator is about 20°C. The actuator is compact and lightweight compared to its pneumatic counterpart.
Originality/value
The MRI compatible actuator uses the B0 field generated by scanner as inductor. The design procedure uses magneto-thermal coupled models that can be adapted to the design of a variety actuation systems working in MRI environment.
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Tassadit Hermime, Abdelghani Seghir and Smail Gabi
The purpose of this paper is the dynamic analysis and seismic damage assessment of steel sheet pile quay wall with inelastic behavior underground motions using several…
Abstract
Purpose
The purpose of this paper is the dynamic analysis and seismic damage assessment of steel sheet pile quay wall with inelastic behavior underground motions using several accelerograms.
Design/methodology/approach
Finite element analysis is conducted using the Plaxis 2D software to generate the numerical model of quay wall. The extension of berth 25 at the port of Bejaia, located in northeastern Algeria, represents a case study. Incremental dynamic analyses are carried out to examine variation of the main response parameters under seismic excitations with increasing Peak ground acceleration (PGA) levels. Two global damage indices based on the safety factor and bending moment are introduced to assess the relationship between PGA and the damage levels.
Findings
The results obtained indicate that the sheet pile quay wall can safely withstand seismic loads up to PGAs of 0.35 g and that above 0.45 g, care should be taken with the risk of reaching the ultimate moment capacity of the steel sheet pile. However, for PGAs greater than 0.5 g, it was clearly demonstrated that the excessive deformations with material are likely to occur in the soil layers and in the structural elements.
Originality/value
The main contribution of the present work is a new double seismic damage index for a steel sheet pile supported quay wharf. The numerical modeling is first validated in the static case. Then, the results obtained by performing several incremental dynamic analyses are exploited to evaluate the degradation of the soil safety factor and the seismic capacity of the pile sheet wall. Computed values of the proposed damage indices of the considered quay wharf are a practical helping tool for decision-making regarding the seismic safety of the structure.
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Mingming Zhao, Fuxiang Wu and Xia Xu
Complex technology not only provides potential economic benefits but also increases the difficulty of application. Whether and how upstream technological complexity affects…
Abstract
Purpose
Complex technology not only provides potential economic benefits but also increases the difficulty of application. Whether and how upstream technological complexity affects downstream manufacturers' innovation through vertical separation structure is worth discussing, but it has not been effectively discussed.
Design/methodology/approach
Through theoretical analysis and empirical testing, this article discusses the cost effect and market competition effect caused by upstream technological complexity on downstream manufacturers and further elucidates the impact of upstream technological complexity on downstream manufacturers' innovation.
Findings
Research has found that the impact of upstream technological complexity on the downstream manufacturers' innovation depends on the cost effect and market competition effect. The cost effect caused by the complexity of upstream technology inhibits the innovation of downstream manufacturers. In contrast, the market competition effect promotes the innovation of downstream manufacturers. There are differences in the cost effect and market competition effect of upstream technological complexity on different types of downstream manufacturers, so there is also significant heterogeneity in the impact of upstream technological complexity on innovation of different types of downstream manufacturers.
Originality/value
The conclusions of this article improve the understanding of the relationship between upstream technological complexity and downstream innovation and provide helpful implications for industrial chain innovation.
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Rizwan Ali, Jin Xu, Mushahid Hussain Baig, Hafiz Saif Ur Rehman, Muhammad Waqas Aslam and Kaleem Ullah Qasim
This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates…
Abstract
Purpose
This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.
Design/methodology/approach
In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.
Findings
This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.
Originality/value
According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.
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Khalil Idrissi Gartoumi, Mohamed Aboussaleh and Smail Zaki
This paper aims to explore a framework for implementing Lean Construction (LC) to provide corrective actions for quality defects, customer dissatisfaction and value creation…
Abstract
Purpose
This paper aims to explore a framework for implementing Lean Construction (LC) to provide corrective actions for quality defects, customer dissatisfaction and value creation during the construction of megaprojects.
Design/methodology/approach
This paper presents a case study involving the construction of the Mohamed VI Tower in Morocco. It is the tallest tower in Africa, with 55 floors and a total height of 250 m. This study of the quality of the work and the involvement of the LC was carried out using the Define–Measure–Analysis–Improve–Control approach from Lean six sigma. It describes the Critical to Quality and analyses the root causes of quality defects, customer dissatisfaction and variation in the quality process.
Findings
Firstly, the results of this study map the causal factors of lack of quality as established in the literature. Secondly, the LC tools have reduced non-value-added sources of quality waste and, consequently, improved critical quality indicators.
Research limitations/implications
This document focuses on one part of the tower’s construction and is limited to a project case in a country where LC is rarely used.
Originality/value
This study reinforces the literature reviews, surveys and the small number of case studies that have validated the potential of LC and further clarifies future directions for the practical emergence of this quality improvement approach, especially for large-scale projects.
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Pratheek Suresh and Balaji Chakravarthy
As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a…
Abstract
Purpose
As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.
Design/methodology/approach
This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R2) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.
Findings
The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.
Research limitations/implications
The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.
Originality/value
The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.
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Jianquan Guo and He Cheng
The authors investigate the effects of Chinese acquirer’s chief executive officer (CEO) risk preference on mergers and acquisitions (M&A) payment method and the moderating roles…
Abstract
Purpose
The authors investigate the effects of Chinese acquirer’s chief executive officer (CEO) risk preference on mergers and acquisitions (M&A) payment method and the moderating roles played by acquirer’s ownership, industry relatedness and whether the M&A is cross-border.
Design/methodology/approach
Using 4,624 worldwide M&A deals conducted by Chinese firms from 2009 to 2021, the authors conduct multiple linear regression and ordered probit regression. And comprehensive indexes constructed based on the observed features of acquirer’s CEOs are used to be the proxy for CEO risk preference.
Findings
The results show that the higher-level Chinese acquirer’s CEO risk preference is overall positively associated with using more stock in payment. Moreover, the above relationship is strengthened if the ownership of the acquirer is state-owned.
Originality/value
The authors highlight the importance of the non-economic factors and demonstrate a relationship between the Chinese acquirer’s CEO risk preference and the M&A payment method, providing support for and enriching the upper echelons theory (UET). Moreover, the unique risk priorities of Chinese acquirers’ CEOs are revealed.
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Md. Nurul Islam, Guangwei Hu, Murtaza Ashiq and Shakil Ahmad
This bibliometric study aims to analyze the latest trends and patterns of big data applications in librarianship from 2000 to 2022. By conducting a comprehensive examination of…
Abstract
Purpose
This bibliometric study aims to analyze the latest trends and patterns of big data applications in librarianship from 2000 to 2022. By conducting a comprehensive examination of the existing literature, this study aims to provide valuable insights into the emerging field of big data in librarianship and its potential impact on the future of libraries.
Design/methodology/approach
This study employed a rigorous four-stage process of identification, screening, eligibility and inclusion to filter and select the most relevant documents for analysis. The Scopus database was utilized to retrieve pertinent data related to big data applications in librarianship. The dataset comprised 430 documents, including journal articles, conference papers, book chapters, reviews and books. Through bibliometric analysis, the study examined the effectiveness of different publication types and identified the main topics and themes within the field.
Findings
The study found that the field of big data in librarianship is growing rapidly, with a significant increase in publications and citations over the past few years. China is the leading country in terms of publication output, followed by the United States of America. The most influential journals in the field are Library Hi Tech and the ACM International Conference Proceeding Series. The top authors in the field are Minami T, Wu J, Fox EA and Giles CL. The most common keywords in the literature are big data, librarianship, data mining, information retrieval, machine learning and webometrics.
Originality/value
This bibliometric study contributes to the existing body of literature by comprehensively analyzing the latest trends and patterns in big data applications within librarianship. It offers a systematic approach to understanding the state of the field and highlights the unique contributions made by various types of publications. The study’s findings and insights contribute to the originality of this research, providing a foundation for further exploration and advancement in the field of big data in librarianship.
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Fu Yang and Mengqian Lu
Drawing on conservation of resources theory, this study aims to develop a resource-based model depicting a decreased level of psychological resourcefulness – relational energy, as…
Abstract
Purpose
Drawing on conservation of resources theory, this study aims to develop a resource-based model depicting a decreased level of psychological resourcefulness – relational energy, as a novel explanatory mechanism that accounts for the harm of abusive supervision, and we further investigate the role of leader humor as a boundary condition.
Design/methodology/approach
We applied multilevel path analysis to test our hypotheses with three-time-point survey data collected from 226 supervisor-employee dyads in a telecommunication company in China across six months.
Findings
Our results show that abusive supervision is negatively related to employee relational energy, leading to a subsequent decline in employee job performance. The predictions of the depleting effects get alleviated by leader humor.
Practical implications
This study foregrounds the importance of employee relationship management in the workplace and reveals that some abusive supervisors may manage to sustain employee performance and relational energy by using humor in their interactions, which necessitates immediate intervention.
Originality/value
These findings offer novel insights into the deleterious impact of abusive supervision by demonstrating the critical role of relational energy in dyadic interactions. We also reveal the potential dark side of leader humor in the context of abuse in the workplace.
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Edward Shih-Tse Wang and Yu-Jou Weng
Increasing followers' positive word-of-mouth (PWOM) is a critical means through which social media influencers (SMIs) marketers can increase SMIs' follower count. Studies have…
Abstract
Purpose
Increasing followers' positive word-of-mouth (PWOM) is a critical means through which social media influencers (SMIs) marketers can increase SMIs' follower count. Studies have reported that authenticity and credibility increase followers' PWOM and have identified the dimensions of authenticity (i.e. originality, naturalness and continuity) and credibility (i.e. attractiveness, trustworthiness and expertise). However, the mechanisms underlying the associations among these dimensions are unclear. Drawing from social exchange theory, the authors developed an integrated conceptual model and explored how the dimensions of SMI authenticity affect those of followers' perception of credibility. Moreover, the authors analyzed how followers' perception of credibility affects the followers' PWOM behavior.
Design/methodology/approach
The authors collected 463 valid questionnaires from respondents that followed at least one SMI. Additionally, the authors developed a structural equation model for data analysis.
Findings
The results revealed that the subdimensions of SMI authenticity have different effects on followers' perception of credibility. An SMI's continuity positively affects followers' perceptions of the SMI's trustworthiness and expertise. The naturalness of an SMI positively affects followers' perception of the SMI's attractiveness but nonsignificantly affects followers' perception of the SMI's trustworthiness. Additionally, an SMI's originality positively affects followers' perception of the SMI's attractiveness but negatively affects followers' perception of the SMI's trustworthiness. Finally, followers' perceptions of an SMI's attractiveness, trustworthiness and expertise all positively affect followers' PWOM behavior.
Originality/value
By employing multidimensional constructs, the authors obtained results that provide a comprehensive understanding of the effects of SMI authenticity on the SMI's followers' perception of followers' credibility. These results can be used by SMIs to increase SMIs followers' PWOM by determining which aspects of authenticity and credibility SMIs should develop.
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