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This bibliometric analysis aims to comprehensively explore the intersection of artificial intelligence (AI) and high-to-room-temperature superconductors. Focusing on scientific literature, the study investigates trends, collaboration patterns and impactful publications in this interdisciplinary field.

The research employs an advanced search query in the Scopus database, targeting articles on the development of superconductors using artificial intelligence. Data collection involves executing the query, saving the results as a CSV file and analyzing it using R-Studio and VOSviewer. Statistical tools, T-tests, regression analysis and Python coding are utilized to enhance the depth of analysis.

The analysis spans various dimensions, including the overview of bibliometric characteristics, annual scientific production, average citations per year, sources of publications and source production over time. Noteworthy findings include a sustained growth in annual scientific production, a peak in average citations in specific years and the identification of influential journals shaping the field.

While the analysis provides valuable insights, limitations include the potential influence of research biases and the exclusion of non-English articles. Further exploration is encouraged to address these limitations and gain a more nuanced understanding of the field.

Practically, this study aids researchers, practitioners and stakeholders in staying informed, identifying collaboration opportunities and contributing meaningfully to the ongoing growth and impact of high-to-room-temperature superconductors using artificial intelligence.

Socially, the study underscores the collaborative and global nature of research in this field, emphasizing the shared endeavor worldwide to advance the understanding and application of superconductors through artificial intelligence.

This research contributes to the originality of the scientific landscape by offering a comprehensive analysis of the development of high-to-room-temperature superconductors with artificial intelligence. The utilization of advanced bibliometric techniques and the identification of key trends and sources enhance the understanding of this emerging and interdisciplinary research domain.

To overcome the shortcomings of traditional dynamic parameter identification methods in accuracy and efficiency, this paper aims to propose a dynamic parameter identification method based on improved iterative reweighted least squares (IIRLS) algorithm.

First, Newton–Euler method is used to establish the dynamic model of the robot, which is linearized and reorganized. Then, taking the improved Fourier series as the excitation trajectory, the optimization model with objective function is established and optimized. Then, the manipulator runs the optimized trajectory and collects the running state of the joint. Finally, the iterative process of iterative reweighted least squares (IRLS) algorithm is improved by combining clustering algorithm and matrix inversion operation rules, and the dynamic model of robot is identified by using the processed collected data.

Experimental results show that, compared with the IRLS algorithm, the root mean square of the proposed IIRLS algorithm is reduced by 4.18% and the identification time is reduced by 94.92% when the sampling point is 1001. This shows that IIRLS algorithm can identify the dynamic model more accurately and efficiently.

It effectively solves the problem of low accuracy and efficiency of parameter identification in robot dynamic model and can be used as an effective method for parameter estimation of robot dynamic model, which is of great significance to the research of control method based on robot model.

This study aims to investigate the relationship between melt pool dimensions and acoustic emission (AE) signal magnitudes obtained during laser powder bed fusion (L-PBF) process of austenitic stainless steel. Specifically, by varying laser power and scan speed, the widths, depths and aspect ratios of melt pools were compared with AE signal magnitudes across a frequency range of 100–200 kHz.

Experiments were conducted under different laser powers at a fixed scan speed and scan speeds at a fixed laser power. Melt pool dimensions were measured from cross-sectional optical images, and AE signals were obtained using a piezoelectric AE sensor installed beneath the build plate. Short-Time Fourier Transform (STFT) was applied to AE signals, and the magnitudes of frequency components were obtained.

A strong correlation between melt pool dimensions and STFT magnitude was obtained. Pearson correlation coefficients between melt pool dimensions and STFT magnitudes were above 0.9 and the p-values were below 0.05. Increasing the laser energy enlarged the volume of melt pool and intensified the oscillation of melt pool. When scan speed exceeded 1,100 mm/s, STFT magnitude showed a slight increase owing to the increase in the vapor pressure.

Previous studies used AE signals to detect defects, but this study found a correlation between STFT magnitude and melt pool dimensions in L-PBF process. It was also found that STFT magnitude was more affected by vapor pressure at higher scan speeds. Monitoring STFT magnitude can help to understand melt pool dynamics, maintain process consistency and identify irregularities in real time.

The purpose of this study is to enhance the safety and comfort of tourists in scenic areas undergoing renovation and transformation by developing a comprehensive safety assessment model that takes into account both internal and external factors affecting tourist and construction safety.

The research employs a multi-level tourist-construction interaction safety assessment index system, which is constructed through a deep analysis of factors such as the construction environment, tourist behavior and safety signs. The study utilizes game theory in conjunction with three main objective and subjective weight distribution methods to determine the weights of the index system, ensuring the objectivity and effectiveness of the assessment results. The cloud model and cloud generator are applied for the language transformation of the indicators, leading to a comprehensive assessment of construction safety.

The survey results indicate that the safety risks of the case project are relatively high, with limited impact of time segments on safety risks, and the risk level during weekends is slightly higher than on weekdays, but the difference is not significant. Among the reviewed influencing factors, compliance with safety signs and the proportion of people crossing construction areas are the factors with the highest risk level, representing a large number of tourists ignoring safety guidance and forcibly crossing construction areas, facing construction dangers, posing a great challenge to safety management.

This study offers a novel methodological approach to safety risk assessment in similar environments, contributing to the field by improving the systematicness and scientific nature of safety management. It provides a scientific assessment tool for the safety management of tourists in scenic area renovation projects, aiming to achieve the dual objectives of tourist safety and construction efficiency.

This study aims to get a better understanding of the impact of streamlined high-speed trains (HSTs) with geometric uncertainty on aerodynamic performance, as well as the identification of the key parameters responsible for this impact. To reveal the critical parameters, this study creates a methodology for evaluating the uncertainty and sensitivity of drag coefficient induced by design parameters of HST streamlined shapes.

Bézier curves are used to parameterize the streamlined shape of HSTs, and there are eight design parameters required to fit the streamlined shape, followed by a series of steady Reynolds-averaged Navier–Stokes simulations. Combining the preparation work with the nonintrusive polynomial chaos method results in a workflow for uncertainty quantification and global sensitivity analysis. Based on this framework, this study quantifies the uncertainty of drag, pressure, surface friction coefficient and wake flow characteristics within the defined ranges of streamline shape parameters, as well as the contribution of each design parameter.

The results show that the change in drag reaches a maximum deviation of 15.37% from the baseline, and the impact on the tail car is more significant, with a deviation of up to 23.98%. The streamlined shape of the upper surface and the length of the pilot (The device is mounted on the front of a train’s locomotive and primarily serves to remove obstacles from the tracks, thereby preventing potential derailment.) are responsible for the dominant factors of the uncertainty in the drag for HSTs. Linear regression results show a significant quadratic polynomial relationship between the length of the pilot and the drag coefficient. The drag declines as the length of the pilot enlarges. By analyzing the case with the lowest drag, the positive pressure area in the front of pilot is greatly reduced, while the nose tip pressure of the tail is enhanced by altering the vortices in the wake. The counter-rotating vortex pair is significantly attenuated. Accordingly, exerts the impacts caused by geometric uncertainty can be found on the wake flow region, with pressure differences of up to 900 Pa. The parameters associated with the shape of the upper surface contribute significantly to the uncertainty in the core of the wake separation region.

The findings contribute to a better understanding of the impact of streamlined HSTs with geometric uncertainty on aerodynamic performance, as well as the identification of the key parameters responsible for this impact. Based on this study, future research could delve into the detailed design of critical areas in the streamlined shape of HSTs, as well as the direction of shape optimization to more precisely and efficiently reduce train aerodynamic drag under typical conditions.

This study, a narrative literature review, aims to examine the combined benefits of the active and passive use of social media (SM) for well-being (WB), physical and mental health during the COVID-19 pandemic.

A search strategy has been carried out in the databases: Riss, PubMed, Medline, Scopus and Google Scholar, including all the articles published until 19 October 2023.

SM offers various benefits, including global risk awareness, health information, social connections and support. With the natural increase in physical inactivity due to COVID-19 social restrictions, SM has been identified as an appropriate tool for promoting physical activity (PA) at home to improve health.

It suggests that the combined use of active and passive benefits of SM could potentially play an important role in public health by increasing individuals’ health behaviours. In addition, dissemination, sharing and social interaction of information provided by YouTube can encourage healthy behaviours, contribute to WB, physical and mental health and raise public health awareness.

The findings presented in this study highlight the combined benefits of differentiating the features of SM use. Compared to other SM platforms, YouTube can be used as a useful tool for home-based PA that promotes health by enabling people to remain active and avoid barriers to PA due to social restrictions during the global crisis. In addition, some recommendations from the findings may help protect against potential risks and improve public health outcomes during global crises, such as the COVID-19 pandemic, among the general public using SM.

Social networks can not only mobilize individuals for collective action but also pose risks, potentially leading to political challenges and societal unrest. Information consumption varies across platforms, with platform characteristics influencing user interactions and information sharing; yet this has received limited attention in scholarly literature. Acknowledging platform-specific differences, this paper seeks to enhance our understanding of the mechanisms driving information diffusion on social networks in the context of geopolitical tensions.

The structural communication features on Twitter and Reddit are explored using schema theory and the concept of social media platform schema. Comparisons are drawn with social network analysis and content analysis of communication dynamics surrounding geopolitical tensions in India–Qatar relations, followed by the context of geopolitical tensions between India and Pakistan.

The results illustrate how content-based connections on Reddit foster closer ties within subreddits but less connectivity between them, contrasting with Twitter’s profile-based connections. These distinct characteristics lead to varied information diffusion patterns and shape the diversity of opinions, influencing community structures and affecting the emotional tenor of discourse.

Social networks can potentially influence geopolitical events, but focusing on one platform overlooks differences in how information spreads and the influence each platform holds. Recognizing this, our comparative analysis of social networks’ structural attributes highlights their crucial roles in shaping user engagement and information diffusion. It lends theoretical support to the notion of social media platform schema with empirical insights into how users’ perceptions of these schemas impact thematic and emotional differences in platform discourse related to geopolitical tensions.

Currently, engineering project design lacks a design framework that fully combines subjective experience and objective data. This study develops an aided design decision-making framework to automatically output the optimal design alternative for engineering projects in a more efficient and objective mode, which synthesizes the design experience.

A database of design components is first constructed to facilitate the retrieval of data and the design alternative screening algorithm is proposed to automatically select all feasible design alternatives. Then back propagation (BP) neural network algorithm is introduced to predict the cost of all feasible design alternatives. Based on the gray relational degree-particle swarm optimization (GRD-PSO) algorithm, the optimal design alternative can be selected considering multiple objectives.

The case study shows that the BP neural network-cost prediction algorithm can well predict the cost of design alternatives, and the framework can be widely used at the design stage of most engineering projects. Design components with low sensitivity to design objectives have been obtained, allowing for the consideration of disregarding their impacts on design objectives in such situations requiring rapid decisions. Meanwhile, design components with high sensitivity to design objective weights have also been obtained, drawing special attention to the effects of changes in the importance of design objectives on the selection of these components. Simultaneously, the framework can be flexibly adjusted to different design objectives and identify key design components, providing decision reference for designers.

The framework proposed in this paper contributes to the knowledge of design decision-making by emphasizing the importance of combining objective data and subjective experience, whose significance is ignored in the existing literature.

Artificial intelligence (AI) in mobile apps is growing rapidly, with features such as image recognition, personalized notifications and prescriptive analytics becoming more common. One such app is the Equalizer AI-powered mobile app, which uses AI to process water invoices, advise customers on fair prices and consumption and allow for online payment and data submission. This study aims to develop a technology adoption model for AI-powered mobile apps in the water sector by extending the value-based adoption model (VAM) to include customer trust.

Primary data was collected from 385 smartphone-using water customers. A stratified sampling approach ensured a representative sample of Palestinian water customers in the West Bank region. The study used a validated tool to measure perceived customer value, trust and adoption intention. It also used structural equation modeling to develop a causal diagram using the AMOS software.

The results confirmed a positive relationship between perceived usefulness, perceived innovation and perceived value and a negative relationship between perceived technical difficulty and perceived value. Contrary to VAM theory, the study showed a positive relationship between perceived fees and perceived value, indicating that users view premium fees as a cue of quality, accuracy, innovation and trustworthiness.

The high adoption intention of these apps holds significant implications for both the government and the water sector. This is because it results in the accumulation of substantial data, which can be used by government authorities and water providers to monitor and sustain the sector effectively.

This research extends existing technology adoption models by integrating customer trust and applying them to the water sector in a developing country. It offers new insights into public service innovations, addressing the unique cultural and sectoral challenges in this context.

Start-ups are successful in receiving valuation in billions of US dollars prior to initial public offering (IPO). However, to sustain higher valuation, the stocks need to perform consistently after the IPO. Short-run stock performance of India-based start-ups during the first year of IPO listing from March 2021 to March 2022 is analysed.

The paper deals with the new generation start-ups' stock performance in emerging market in terms of total and abnormal return generated in comparison to the market (NIFTY-200). Further, the volatility of returns during bear and bull regimes is analysed through a family of Markov-switching GARCH models using both normal and skewed distributions.

The results suggest that start-up stocks are more volatile during bear regime than in the bull run in market-based economies where price limit policy does not apply. Besides, the cumulative abnormal return over the market return was lower for majority of start-up IPO stocks.

Though negative returns of the start-up stocks during the first year of IPO need not be surprising, higher volatility during bear regime is a matter of concern as it could severely impact retail investors and founders. The results hold implication for IPO regulation in emerging markets and for retail investors desirous of investing in start-up stocks.

Volatility of return is examined using a state-space model during the first year of the start-up IPO listing. The study contributes to the emerging market IPO literature by examining IPO performance in market-based economy. Previous IPO performance studies in emerging markets are predominantly based on ecosystems where start-ups are subjected to price limit policy, and it does not reflect the true nature of IPO performance across emerging markets.