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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.

Shopping through e-commerce platforms has become a primary daily activity. However, research on consumer engagement within e-commerce platform contexts remains scarce. We examine the relationship between consumer engagement on online shopping platforms and their subjective well-being, considering self-expansion and self-extension as mediators.

We investigate the role of consumer engagement by dividing it into two experiences (crowdsourcing and crowdsending). Using validated measurement scales to analyze data from 440 South Korean consumers, we examine how these engagement experiences affect self-expansion and self-extension, ultimately leading to higher subjective well-being.

Crowdsourcing and crowdsending play different and complementary roles in improving self-concept. Furthermore, self-expansion and self-extension are key variables influencing consumer engagement and well-being on the platform.

This study provides a new perspective of consumer online shopping behavior, revealing the self-related mechanisms that influence the relationship between consumer engagement experiences and subjective well-being.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

In the context of growing efforts by online businesses to enhance consumer connections, understanding consumer engagement behaviors is imperative. This study explores consumer engagement within online shopping platforms, specifically introducing and examining the roles of crowdsourcing and crowdsending.

The study developed and validated measurement scales for crowdsourcing and crowdsending engagement across transactional, multi-sided and inspirational platforms.

Identifying five sub-dimensions within crowdsourcing and crowdsending, the results unveiled nuances in consumer–platform interactions, emphasizing the value of co-creation. Crowdsourcing entails transaction-oriented engagements such as knowledge gathering, utilitarian and hedonic browsing, interaction and co-shopping. The findings revealed that crowdsourcing significantly influenced platform commitment, surpassing the impact of crowdsending on transactional platforms. Conversely, crowdsending involves knowledge sharing, feedback, participation, advocacy and reciprocity, fostering active engagement and shared value within the platform ecosystem. Notably, the results showed that crowdsending strengthened commitment to inspirational platforms more than to conventional shopping platforms.

This study contributes to the theoretical understanding of a range of consumer engagement experiences in online shopping environments and presents practical applications, offering valuable insights for commerce businesses aiming to optimize their digital strategies.

This study aims to investigate the intentions of Muslim cryptocurrency (CC) holders to fulfil their zakat obligations on digital assets, exploring the unique motivations and barriers within this emerging financial landscape.

The research uses a quantitative approach and a cross-sectional research design through online surveys, using purposive sampling to gather data from Muslim CC holders. The integrated model, known as the theory of planned behaviour and social cognitive theory (TPB-SCT) model, is used to comprehensively analyse the key factors influencing intentions to pay zakat on cryptocurrencies (CCs).

The study reveals that attitude towards zakat on CCs and perceived behavioural control regarding zakat on CCs have a significant and positive effect on the intention to pay. In contrast, subjective norms show no significant influence. CCs-related financial risk exerts a negative impact on intention. Moreover, CCs-related zakat knowledge and adherence to Shariah compliance are strongly associated with intention. These findings provide insights into the intricate dynamics of religious compliance within the evolving realm of digital assets.

Outcomes offer profound indications to stakeholders, including financial institutions, zakat agencies, policymakers and the community, on how to integrate zakat into this new and rapidly evolving financial paradigm like CC.

A pioneering effort was made in this study by exploring the intentions of Muslim CC holders to fulfil zakat obligations, bridging a significant gap in the existing literature. Developing and validating an integrated model of TPB-SCT in the realm of zakat on CC enriches the literature with a novel theoretical framework.

This study reveals the trigger of innovative behavior from the perspective of intrinsic and extrinsic spiritual inspiration and provides a new research idea for the formation mechanism of innovative behavior. The purpose of this study is to provide certain guidance and implications for enterprises to cultivate and enhance employees’ innovative behavior.

We conducted three studies, collected multi-source data (N = 1,175) from different countries longitudinally, as well as used hierarchical regression analysis and fuzzy-set quantitative comparative analysis to verify the theoretical model.

According to the findings, both spiritual leadership and career calling have a positive impact on employees’ innovative behavior through the mediating effect of autonomous motivation and the moderating effect of person-vocation fit.

Innovative behavior is the positive professional pursuit of employees, which is difficult to form without the motivation of spiritual factors. Spirituality is a complex concept that contains intrinsic and extrinsic spiritual factors, both of which could stimulate employees’ innovative behavior. Although many discussions have been held on this topic in recent years, little attention has been paid simultaneously to the motivating effects of the two perspectives. Drawn from self-determination theory, this study explores the mechanisms of two spiritual motivation paths (i.e. the intrinsic and extrinsic spiritual motivation paths) in the improvement of employees’ innovative behavior.

Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.

This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.

In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.

To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.

Emergencies have become a growing concern for organizations, which require flexibility to respond to changes in emergencies based on their contingency, dynamic evolution rapidly and other characteristics. In order to enhance the ability of engineering project organizations to cope with emergencies, this study explores the mechanism of its influence on knowledge innovation under emergencies from the perspective of bricolage theory, and provides a new perspective for the traditional preplanning-based handling of emergencies by improvising to enhance the ability and results of improvisation.

Firstly, a structural equation model of the relationship between bricolage and knowledge innovation was constructed by introducing improvisational behavior and serendipity as mediating and moderating variables of the relationship between bricolage and knowledge innovation based on bricolage theory; secondly, drawing on previous well-established measurement scales about bricolage, improvisational behavior, knowledge innovation and serendipity, a questionnaire survey was conducted with different types of engineering project managers and technicians in Gansu Province as the research subjects, and 238 valid questionnaires were returned; finally, validation factor analysis and correlation analysis were performed, and the hypothesized relationships were verified using AMOS 24.0 software.

The results show that bricolage positively influences improvisational behavior; improvisational behavior positively influences knowledge innovation; bricolage positively influences knowledge innovation; bricolage influences knowledge innovation through the mediating role of improvisational behavior and serendipity positively moderates the impact of resource bricolage on knowledge innovation.

It reveals the mechanism of knowledge innovation of engineering project organizations in response to emergencies and the innovation mechanism of the episodic nature of emergency decision-making, extends the applicable context of bricolage theory and provides a new perspective for engineering project organizations in response to emergencies.

Open-source communities are platforms that promote knowledge sharing. The mitigation of open-source risks is crucial to these communities. Therefore, this article explores the governance mechanisms of knowledge sharing in open-source communities.

To answer the core research question – “What are the governance mechanisms of knowledge sharing in open-source communities?” – we conducted an in-depth case study analysis of two open-source communities based in China.

Two types of open-source communities were found: technology-driven communities and enterprise ecosystem-oriented communities. Hence, their governance mechanisms differed. For the former type, it was important to integrate social and commercial value to encourage knowledge exchange and enhance business scenarios through community-user experience. For the latter type, mutual collaboration and knowledge sharing could be fostered through differentiated layouts and the distributed collaboration of developers around data-driven innovation scenarios. This required the integration of individual and ecosystem value through value exchange.

This study advances our understanding of the coordinated development between founding firms and digital technology-based open-source communities. The findings offer important guidance to business practitioners seeking to manage knowledge-sharing activities during digital transformations.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.