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For underwater hydraulic machinery, the unique structure significantly enhances the three-dimensional non-uniformity of turbulence within the flow domain and high Reynolds number turbulence introduces complex effects on the machinery. Therefore, studying the turbulent flow characteristics in underwater hydraulic machinery is crucial for system stability.

This paper conducts a numerical analysis on a specific type of underwater hydraulic machinery. A numerical calculation model is established under stable inflow conditions to analyze the flow trends and pressure changes at different flow speeds. Subsequently, structural modifications are made to the underwater hydraulic machinery, and the characteristics of the velocity field, pressure field and vorticity distribution under different model parameters are analyzed.

The results indicate that changes in internal structure have a certain impact on flow characteristics. When the structural changes are significant, the fluid flow becomes more complex and pressure fluctuations become more intense. The research findings provide a scientific basis and theoretical guidance for the structural design of underwater hydraulic machinery and have significant research implications for controlling fluid-induced noise.

Affected by the inherent structural characteristics of the flow channel structure, the flow direction of the high-speed water flow changes drastically in the flow channel, so it is of great significance to study its flow characteristics for the stability of the system.

This study aims to deeply explore the factors influencing mobile game players' willingness to make in-game purchases, providing references for game developers and marketers to formulate effective strategies.

This research integrates the coolness factors and hedonic motivation system acceptance model to construct a comprehensive theoretical model analyzing mobile game players' willingness to make in-game purchases. The framework includes multidimensional variables such as joy, coolness, immersion, and game experience. Using data from 392 surveys collected from mobile game forums and social networks, the study employs structural equation modeling to analyze the factors and mechanisms influencing players' willingness to make in-game purchases and to verify the related research hypotheses.

The findings reveal that coolness factors have a significant positive impact on game experience and immersion, which in turn affect players' willingness to make in-game purchases. Game experience has a significant positive impact on both immersion and purchase willingness. A good game experience not only increases players' immersion but also directly enhances their willingness to make in-game purchases. Immersion plays a mediating role in the influence of coolness factors and joy on purchase willingness.

By integrating coolness theory with the hedonic motivation system acceptance model, this study constructs a comprehensive model to explore mobile game players' willingness to make in-game purchases. The combination of variables, including personal psychological and social psychological factors, provides a thorough analysis of the factors influencing mobile game purchase willingness, enriching existing research.

The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell.

In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making.

The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest.

In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.

The substantial expansion of technology and the efficacy of digital platforms in reaching young audiences have led to enhanced targeting and customization of promotional communications. Notwithstanding the expansion and efficacy of contemporary advertising platforms, scholarly attention has not kept pace with this domain of inquiry. This study aims to assess the antecedents of Google Shopping Ads (GSA) on intention to purchase behavior among the Generation Y and Z cohorts.

The current study used a quantitative approach and snowball sampling technique to gather primary data via a questionnaire and Google Forms, which resulted in the collection of 5,808 questionnaires among the cohort members. A principal component analysis and multigroup confirmatory multigroup structural equation modeling (between Generation Y and Z) were used to assess the research data and model.

The results show positive trust and perceived value associations with intention to purchase, particularly among Generation Y and Z consumers. The findings also show negative irritation, product risk and time risk associations with intention to purchase, especially among the Generation Y cohort, which indicates that young consumers generally do not observe perceived risk due to the usage of GSA.

GSA will continue to grow and become an increasingly important integrated marketing communications tool as the digital landscape develops. It can be concluded that young consumers show a high degree of perceived value and low levels of perceived risk due to the use of GSA. This study, therefore, promotes improved understanding among academics, marketers and businesses of search engine advertising among young cohorts of consumers (Generation Y and Z) in a developing country context.

Eddy currents are inevitable in magnetic resonance imaging (MRI) systems. These currents are mainly induced by gradient fields. This study aims to propose a fast analytical method to calculate eddy currents induced by frequently switching gradient fields in a traditional C-shape MRI system.

Fourier decomposition and magnetic vector potentials were used to calculate the eddy currents. Calculations with the proposed analytical method revealed the spatial distribution and temporal evolution of eddy currents.

Calculation and Maxwell simulation results were consistent. The agreement between calculation and simulation results indicates that increasingly sophisticated structures could be developed. The calculated results could guide the design of improved gradient coils.

Eddy currents induced by gradient current are decomposed into currents induced by each time-harmonic component, and then adding them together to obtain complete contribution of the eddy current. The analytical method was used to characterize the properties of symmetric and asymmetric eddy currents induced by gradient coils in MRI systems. The analytical method can be used to improve the gradient shield during the design of the gradient coil in the MRI system.

This study aims to conduct a rigorous bibliometric analysis of impulse buying research trends and patterns, map the conceptual landscape of the field, identify significant themes and propose a roadmap for future studies in this domain.

This study used Snyder’s (2019) four-step systematic review approach. By using a specific search string, articles from the past 21 years (2003–2023) were retrieved from the Scopus and Web of Science databases. Performance analysis and science mapping were conducted using Biblioshiny to analyze the field’s knowledge base.

The volume and influence of impulse buying research have surged over the past two decades. The factorial analysis identified three main sub-themes within the impulse buying literature. In addition, Bradford’s law confirmed that the top five journals account for a significant portion of the relevant research. Despite the increased publications, author productivity diverges from Lotka’s law, indicating a few influential authors. This study provides a detailed conceptual map of the research landscape and proposes targeted future research questions by highlighting untapped opportunities.

This study offers insights for refining marketing strategies and highlights the importance of ethical considerations in marketing, especially during crises. It also strengthens the theoretical foundation of impulse buying by mapping core themes and identifying unexplored areas.

This study introduces an innovative approach by using factorial analysis with the multiple correspondence analysis technique to develop a detailed conceptual structure map of impulse buying research. It applies bibliometric laws such as Bradford’s law and Lotka’s law to explain the dispersion of research articles. By identifying core themes, this study charts a roadmap for future inquiry, addressing significant gaps and uncovering new research directions.

This study aims to identify the key drivers of consumer engagement in e-commerce among young consumers at bottom-of-pyramid (BoP) markets and their impact on continued usage intention.

A cross-sectional research design was used to understand low-income customers’ engagement in e-commerce, specifically online shopping. The data for this study were collected from BoP customers in the Indian market. A conceptual model was proposed, and hypotheses were developed using the stimulus–organism–response (S-O-R) framework. For analysis, structural equation modeling was performed using AMOS 20.0 software to test the structural model.

The results of the study highlight that perceived importance, technology and infrastructure and social influence are key drivers of e-commerce at BoP customers. Key drivers have shown a significant positive impact on customer engagement which leads to continue usage intention of e-commerce. Furthermore, customer engagement has shown a strong relationship with continue usage intention of e-commerce.

This study indicates that young consumers’ engagement is important for e-commerce service providers to gain a market share. BoP markets offer immense opportunities to create, develop and sustain e-commerce firms for a long time, especially in India. Managers should recognize the potential of BoP markets, which can generate a huge demand for products and services on e-commerce platforms.

This study contributes both theoretically and empirically. Theoretically, this adds to the existing knowledge of customer engagement, especially in e-commerce and BoP market segment. Empirically, it tested the conceptual research model of low-income customer engagement in the e-commerce marketplace using the S-O-R framework. The study recommended practical implications for e-retailers/e-commerce service providers engaging BoP customers in a digitally connected and intensively competitive era.

The main purpose of the study is to investigate the influence of live streaming participation on purchase intention, specifically focusing on how fear of missing out (FOMO) impacts live streaming purchase intention and its subsequent effect on inaction inertia. Additionally, the study aims to ex-amine the indirect influence of FOMO on the relationship between live streaming participation and purchase intention.

The research employs SEM to assess the properties of measures. The investigation consists of four main constructs: live streaming shopping participation, fear of missing out, live streaming purchase intention, and inaction inertia. Live streaming shopping participation is further divided into two dimensions: live streaming immersion and live streaming social presence, with the latter encompassing social presence and telepresence.

The study reveals the significant role of FOMO in livestream shopping. Viewers' fear of missing out on information and products presented in livestreams results in heightened attention to these streams and an elevated willingness to purchase. This finding emphasizes the impact of FOMO emotions in driving consumer action and purchasing intent, particularly in situations of product scarcity.

The study uniquely explores FOMO as a factor disrupting consumer inertia, influencing decisive purchasing. It shows that FOMO enhances perceived value of products, altering consumer behavior in live streaming and e-commerce, thus providing a novel perspective on FOMO’s extended impact.

The COVID-19 pandemic and its aftermath sent the entire educational system across the globe topsy-turvy. Virtual classrooms, online lectures and online evaluations became the order of the day, replacing traditional face to face classroom interactions and examinations conducted physically. While it may be possible to reach out to a larger audience in remote places via online platforms, the new medium lacks personal touch of the past, and is known to cause physical and psychological problems for participants. This study collects primary data from a representative sample of students from emerging economies to study the factors that influence intention to pursue online education.

ANOVA, Kruskal–Wallis test, exploratory factor analysis (EFA) and multiple linear regression (MLR) have been used to test our hypothesis. We have also used text mining to corroborate statistical test results and ascertain the sentiment of students towards online learning.

This study not only confirms findings in extant literature that “benefits” is an important factor. It also identifies new factors such as “health”, “evaluation”, “class duration” and “student qualification”, that influence student intention to pursue online education. Sentiment analysis shows that students have positive sentiment coupled with trust towards online education. Text mining shows that “mode of class”, “time or duration of class” and “quality of learning” are important features that students consider.

This is one of the few studies to use quantitative plus text mining method of research to understand intention to pursue online education.

The authors designed those experiments to test the sensitivity of graphene when it is exposed to NO₂ gas, to find a way to decrease the recovery time of graphene and to find the difference effect between monolayer and bilayer graphene in the experiments.

The authors transferred graphene from film on Cu foil to NO₂ sensor sample and measured the resistances of on monolayer and bilayer graphene when they were exposed to NO₂ gas under different concentration; then, the authors obtained the results.

The results show that monolayer graphene exhibits a linear response when the NO₂ concentration is below 20 ppm. But the monolayer graphene will not be so sensitive to NO₂ gas when the concentration continues to reduce. The desorption time of monolayer graphene is longer when compared with bilayer graphene. It shows faster recovery time and higher response of bilayer graphene under low NO₂ concentration. And the limit detectable NO₂ concentration of bilayer graphene is 50 ppb. Desorption time of bilayer graphene is shortened to below 20 s under UV light.

The authors found a reliable way to decrease the recovery time of graphene when it is exposed NO₂ gas and got the concrete data.