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The purpose of this study is to develop a magnetic resonance imaging (MRI)-safe iron-free electrical actuator for MR-guided surgical interventions.

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.

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.

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.

The digitalization has changed the volunteer paradigm, making young volunteers use technology in their volunteering activities. The current study sets out to identify and model the antecedents that determine intention to engage in digital health volunteering among Algerian physicians to give insights promoting the development of digital volunteering in different countries of the world.

To this end, the authors used a survey design to extend the technology acceptance model (TAM) with two construct (self-efficacy and perceived risk of COVID-19 infection). A convenience sample of 163 physicians; working in the private and public sectors in six provinces of Algeria was selected. The data were analyzed through a multiple linear regression.

The findings show that the perceived usefulness, ease of use, attitudes toward digital volunteering, level of self-efficacy and perceived risk of COVID-19 infection have a significant positive effect on physicians' intentions to engage in digital volunteering work in the context of health crises.

This study reveals that engaging in digital volunteering can be promoted during health crises as an effective strategy to provide support and assist public health institutions and emergency management.

To the best of the authors' knowledge, this is the first study from Africa that explores digital volunteer work, and the first study that extends the TAM to investigate digital volunteer intention among physicians.

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.

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.

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.

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.

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.

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.

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.

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.

This study aims to track the historical development in tourism and hospitality research over the past 30 years by applying a novel interdisciplinary approach, combining both corpus linguistics and bibliometric analysis.

Most frequently discussed topics and newly emerging topics were identified by investigating 18,266 abstracts from 18 leading tourism and hospitality journals with corpus linguistics toolkit AntConc and natural language processing (NLP) tool spaCy. Trend analysis and bibliometric methods were used to determine the longitudinal changes of research topics, most highly-cited publications and authors' production.

This study revealed the evolution patterns of the identified 576 most frequently discussed topics across the four subperiods (1991–2000, 2001–2010, 2011–2015 and 2016–2020). Specifically, results showed that information technology-related topics account for the largest proportion of the identified 38 newly emerging topics from 2011. Besides, researchers are increasingly focusing on the use of more sophisticated and advanced statistical methodologies.

This study helps researchers make sensible decisions on what research topics to explore; it also helps practitioners and stakeholders make the shift and track opportunities in the field.

No other studies have employed the novel interdisciplinary approach, combining corpus linguistic tools in linguistics, NLP techniques in computer science and bibliometric analysis in library and information science, for exploring research trends in tourism and hospitality.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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%.

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.

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.

This study aims to explore whether digital platform capabilities (integration and reconstruction) affect technological innovation through knowledge bases in the dimensions of breadth and depth and the moderating role of organisational routines updating.

Hierarchical regression, mediation effect test macro and bootstrap were conducted to empirically analyse two waves of longitudinal survey data from 179 Chinese technology firms.

Results confirmed that knowledge bases (breadth and depth) mediated the effect of digital platform capabilities (integration and reconstruction) on technological innovation and that updating of organisational routines moderated the relationship between knowledge bases and technological innovation.

These findings offer guidance to firms that aim to achieve technological innovation and advantages, highlighting the importance of digital platform capabilities, knowledge bases and organisational routines updating.

Advancing from existing digital strategies and firm innovation literature, the authors provide a new perspective (knowledge bases) to respond to the information technology (IT) paradox and understand the role of digital platform capabilities in improving technological innovation.

This study tends to investigate how the outbreak of the coronavirus disease 2019 (COVID-19) pandemic has affected banks' contribution to systemic risk. In addition, the authors examine whether the impact of the pandemic may vary across advanced/emerging economies, and with banks with differed characteristics.

The authors construct the bank-specific conditional value at risk (CoVaR) and marginal expected shortfall (MES) to measure their contribution to systemic risk and define the outbreak of the COVID-19 pandemic by the timing when countries report more than 100 confirmed cases. The authors use the approach of difference-in-differences to assess the impact of the COVID-19 pandemic on banks' contribution to systemic risk. This sample comprises monthly panel data of around 900 listed commercial banks in 39 advanced and emerging economies.

The authors find that, firstly, the COVID-19 pandemic increased banks' contribution to systemic risk significantly around the world. Secondly, the impact of the COVID-19 virus was more pronounced in developed countries than in emerging economies. Finally, banks with a larger size and higher loan-to-deposit ratio are more greatly affected by the COVID-19 pandemic, while a higher capitalization for banks is insufficient to shelter them from the adverse impact of such pandemic.

The authors assess the impact of the COVID-19 pandemic on banks' contribution to systemic risk. Using the conditional value at risk (marginal expected shortfall) of banks as the measure, this study’s results suggest that banks' contribution to systemic risk increases by around 25% (48%) amid the COVID-19 pandemic. This study’s findings may shed some light on the potential policies that financial regulators may employ to ameliorate the adverse outcomes of the ongoing pandemic.