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Finite element analysis of underwater transducers typically requires a high level of expertise, and the iterative process of testing various sizes, material parameters and other factors is often inefficient. To address this challenge, this paper aims to introduce underwater transducer parametric simulation (UTPS) software to streamline the design and optimization process.

The design methodology integrates the strengths of ANSYS Parametric Design Language (APDL) for parametric design with the Qt Creator framework for developing a visual interface. C++ is used to encapsulate complex, hard-to-master APDL macros and interact with ANSYS software to execute the relevant APDL macros, performing finite element analysis on the underwater transducer in the background. The results are then processed and displayed on the visual interface.

UTPS enables parametric modeling, modal analysis, harmonic response analysis and directivity analysis of underwater transducers. Users only need to input parameters into the software interface to obtain the transducer’s performance, significantly improving work efficiency and lowering the professional threshold. A prototype transducer was fabricated and tested based on UTPS results, which confirmed the accuracy of the software.

This paper presents an innovative parametric simulation tool for underwater transducers, combining finite element analysis and APDL to simplify and expedite the design process. UTPS reduces the need for specialized knowledge, cutting down on training costs, while its parametric design capabilities accelerate the design process, saving resources.

This study aims to investigate the impact of news quality on users’ risk perceptions toward online news and its subsequent influence on perceived believability and user engagement in sharing news. Additionally, we explore the moderating effects of fake news awareness and social tie variety.

Drawing upon the social amplification of risk framework, this study investigates the relationship between news quality and users’ news-sharing behaviors, along with its underlying mechanism. An online questionnaire involving 399 eligible participants was employed for hypotheses testing, and the structural equation model served as the main analytical method.

The influence of news quality on users’ news-sharing behavior is sequentially mediated by risk perception and perceived believability. Individuals with a heightened awareness of fake news or a diverse social tie are more inclined to perceive greater risks associated with news-sharing behavior and question news authenticity.

This study contributes to the existing literature on users’ news-sharing behaviors by examining the influence of risk perception on the relationship between news quality, perceived believability and users’ news-sharing behavior. Additionally, it explores the moderating effects of fake news awareness and social tie variety. Our findings offer valuable insights into comprehending user inclinations towards news sharing and mitigating the dissemination of fake news.

A tightly coupled global navigation satellite system (GNSS)-Vision-IMU-wheel odometer (GVIWO) system is proposed, which can realize robust positioning in extreme environments. The purpose of this study is to achieve adaptive initialization in complex environments, sensor anomaly detection and processing, and adaptive robust localization in extreme environments.

Adaptive initialization includes traditional dynamic and static initialization and extreme condition initialization. To deal with the unstable visual features in the state of excited motion, a method of wheel odometer assisted initialization is designed. According to the abnormal condition of the sensor, the anomaly detection and attenuation mechanism are designed to realize the accurate positioning of the sensor under abnormal condition.

Tight coupling optimization of GNSS signals, RGB+Depth Map cameras, inertial measurement units and wheel odometers ensures accurate positioning in both indoor and outdoor environments. Through open data sets and field validation experiments, the proposed tightly coupled system has strong adaptability, especially in extreme environments.

A new framework is proposed by integrating GNSS, visual, inertial measurement unit (IMU) and wheel odometer sensors to form an efficient positioning solution. An adaptive initialization method is proposed to enhance the robustness and real-time performance of the positioning system in complex and dynamic environments. A mechanism for detecting and attenuating sensor anomalies is designed, enabling quasideterministic positioning under sensor anomalies.

Providing high-quality service for airline companies cannot be overstated, as it directly impacts their survival and competitiveness. Hence, there is an increasing need to measure loyalty in the air transport industry. Loyal customers are highly valued as they are less price-sensitive and require minimal communication efforts. Despite this, there is limited knowledge about the factors that influence a passenger’s level of loyalty towards an airline company. Therefore, understanding the precursors of loyalty is essential. This research investigates the serial mediation effect of consumer brand identification (CBI) and perceived value on the relationship between service quality and loyalty.

In this research, quality is related to loyalty through value and CBI. A conclusive research design was adopted to determine whether value and CBI have a serial mediating effect on the quality-loyalty link. The research framework includes a three-path mediation model.

Based on the analysis of 406 questionnaires, the study concludes that a serial mediation effect of CBI and perceived value on the quality-loyalty relationship exists. The research results indicate that CBI has a greater influence on improving customer loyalty than perceived value.

This study contributes to limited research on the serial mediating effect of value and CBI in the quality-loyalty relationship. It also provides empirical evidence on the determinants of loyalty as part of the Turkish aviation industry. This study highlights the significance of CBI as a critical factor for airlines to maintain their competitiveness in the market.

The landscape of health information acquisition has shifted from offline to online, and online question-and-answer (Q&A) communities have emerged as prominent sources of health information; however, it is unclear how users identify satisfactory health information. This paper identifies factors that influence users’ adoption of health information in the context of online Q&A communities.

Based on the elaboration likelihood model (ELM) and opinion leader theory, we construct a research model to examine how information quality (complexity, image structure and emotional change) and source credibility (authentication status, follower number) affect health information adoption behavior. We verify the hypotheses by Poisson regression and zero-inflation Poisson regression using the data collected from an online Q&A community.

The empirical results indicate that both information quality and source credibility positively affect users’ adoption of health information.

This research can assist designers and managers of online Q&A communities to better comprehend users’ health information needs and their preferences for adoption. This enhanced understanding can facilitate the provision of superior online health information.

The purpose of this study is to identify the determinants of success in peer-to-peer (P2P) lending campaigns, especially amid global financial disruptions like the COVID-19 pandemic. Addressing a notable gap in current research, we explore how factors such as firm uncertainty, loan characteristics (interest rates and maturity) and venture quality (human, social and intellectual capital) influence P2P lending effectiveness. Using multiple regression analysis on data from 523 projects on the October platform, our study aims to enhance the understanding and operational efficiency of P2P platforms, contributing to a more resilient financial ecosystem.

This study employs a quantitative research design using multiple regression analysis to examine the impact of specific variables on the success of P2P lending campaigns. Data were collected from 523 concluded P2P lending projects on the October platform, spanning from 2015 to 2021. Variables of interest include the level of uncertainty of the firm, loan characteristics such as interest rate and maturity and the quality of the venture assessed through human, social and intellectual capital. This method allows for a robust analysis of the factors contributing to the success of P2P lending within a dynamic financial context.

The findings of this study reveal that the success of P2P lending campaigns is significantly influenced by the level of uncertainty of the firm, the interest rate of the loan and the quality of the venture. Specifically, higher uncertainty in firms correlates negatively with campaign success, while competitive interest rates positively impact funding outcomes. Furthermore, ventures that demonstrate robust human capital, particularly those with management teams that possess diverse skills and high qualifications, tend to attract more funding. These results underscore the critical role of strategic financial and human resource planning in enhancing the effectiveness of P2P lending platforms.

This study contributes uniquely to the literature by integrating multiple variables – firm uncertainty, loan characteristics and venture quality – into a comprehensive analysis of success factors in P2P lending. It addresses the scarcity of research examining the combined effects of these factors, particularly in the context of global financial disruptions like the COVID-19 pandemic. By focusing on a specific European platform during a dynamic period, this research provides new insights into how P2P lending can adapt to and thrive amid financial crises. The findings offer valuable guidance for both practitioners and policymakers aiming to optimize P2P lending practices in uncertain economic landscapes.

Drawing on the tenets of institutional theory, the purpose of this study is to examine the impact of Confucianism on technology for social good, while also considering the moderating influence of extrinsic informal institutions (foreign culture) and intrinsic formal institutions (property rights).

This study constructs a comprehensive database comprising 9,759 firm-year observations in China by using a sample of Chinese A-share listed firms from 2016 to 2020. Subsequently, the hypotheses are examined and confirmed, with the validity of the results being upheld even after conducting endogenous and robustness tests.

The findings of this study offer robust and consistent evidence supporting the notion that Confucianism positively affects technology for social good through both incentive effect and normative effect. Moreover, this positive influence is particularly prominent in organizations with limited exposure to foreign culture and in nonstate-owned enterprises.

The findings contribute to the literature by fostering a deep understanding of technology for social good and Confucianism research, and further provide a nuanced picture of the role of foreign culture and property rights in the process of technology for social good in China.

Amid the increasing water risks faced by firms, external investors are becoming more interested in corporate water disclosure and research on its drivers has become prominent. This paper aims to investigate the impact of water resource tax (WRT) on water disclosure and other related drivers.

This study uses the WRT policy as a quasi-natural experiment and applies the difference-in-differences method.

The results indicate that WRT policy significantly stimulates water disclosure. Improving green innovation and strengthening internal control are potential channels through which WRT works. Moreover, WRT’s effect is more pronounced in firms that face high institutional pressures and have better internal resource support.

The findings suggest that water-sensitive firms should disclose water information to acquire resources from external stakeholders to support their green transition. It also provides implications for governments to incorporate other external forces in shaping the direction and intensity of WRT and consider the resource constraints of small and private firms in green transformation.

This study is of assistance in promoting water environmental protection in areas experiencing water stress and provides an opportunity for external stakeholders (external investors, nongovernmental organizations, governments, consumers, suppliers, communities and media) to advocate the water disclosure of firms with high water risks.

The attempt is novel in the context of considering the water regulation risks and the demands of external stakeholders. It provides new insights into the factors influencing water disclosure from the perspective of political stakeholders.

This study aims to examine the effect of investor sentiment on stock market crash risk in the Asia–Pacific region and the moderating role of uncertainty factors.

This research uses principal component analysis to construct an investor sentiment index of 17 Asia–Pacific stock markets before analyzing the data through hierarchical regression with the feasible generalized least squares method.

The empirical results show that investor sentiment has a positive impact on crash risk in the Asia–Pacific stock markets. This impact is more significant in advanced economies compared to emerging markets and developing economies. The nexus between investor sentiment and stock market crash risk is increased by economic policy uncertainty but decreased by uncertainty from pandemics.

This study only investigates the moderating roles of global uncertainty factors but not local uncertainty factors. In addition, the sentiment of different investor groups has not been examined yet.

The findings of this study are relevant for global investors, portfolio managers and policymakers. Investors and portfolio managers can use sentiment indicators to predict downturns and adjust strategies, whereas policymakers can leverage these insights to design regulations that mitigate systemic risks during global uncertainties.

This study expands the literature on the relationship between investor sentiment and stock market crash risk, providing updated insights into sentimental bias in behavioral finance.

The purpose of this paper is to provide evidence on the relationship between artificial intelligence (AI) and financial inclusion to achieve sustainable development goals (SDGs), an agenda set by United Nations for 2030. Financial inclusion is an enabler of 8 of the 17 SDGs. This paper emphasizes the introduction of AI in the financial sector, which is indispensable for achieving financial inclusion and plays a crucial role in the achievement of SDGs.

This study adopts qualitative research methodology to highlight the significance of AI in achieving high levels of financial inclusion in an economy. Both narrative and comparative approaches are used to provide empirical evidence for reaching the UN SDGs target through AI-assisted financial inclusion.

AI implementation in finance enables people to take part in the formal financial sector and thus, enhances economic growth and reduces poverty.

This research is limited in its data. Only five top AI applications are chosen and comparison is made between two countries only. Future research should consider it as an established concept and include more data to strengthen the evidence.

The results of this paper will help policymakers convince governments and institutions to put their efforts toward AI implementation in financial infrastructure of countries.

This research is unique in providing real-life examples and cases demonstrating the significance of AI implementation in the financial sector. Recent literature lacks evidence on the relationship of AI, financial inclusion and SDGs. This study adds to the existing literature by compiling data on top AI applications and comparing the performance of countries in achieving financial inclusion with the help of AI.