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The purpose of this study is to provide ideas and theoretical guidance for green, environmentally friendly and efficient “bacteriostasis with bacteria” technology.

In this paper, a beneficial strain of bacteria was extracted and purified from marine mud. Weight-loss test, morphological observation and electrochemical test were used to systematically study the effect of sulfate-reducing bacteria (SRB)-induced corrosion inhibition on X65 steel in simulated offshore oil field production water.

The results showed that a beneficial strain was selected and identified as Vibrio alginolyticus. Under the condition of co-culture of SRB, the average corrosion rate of X65 steel was significantly reduced. In the mixed bacterial system, the surface of X65 steel samples was relatively flat, and the structure of biofilm and corrosion product film was dense. The number of corrosion pits, the average diameter and depth of corrosion pits were significantly reduced. The localized corrosion of X65 steel was significantly inhibited.

The complex and changing marine environment makes the corrosion problem of marine steel increasingly severe, and the microbiologically influenced corrosion (MIC) caused by SRB is particularly serious. The research and development of environmentally friendly corrosion protection technology is a long-term and difficult problem. The use of beneficial microorganisms to control MIC is a green and efficient anticorrosion measure. Compared with terrestrial microorganisms, marine microorganisms can adapt to complex environments, and their metabolites exhibit special biological activities. The use of marine beneficial bacteria can inhibit SRB activity to achieve the corrosion inhibition effect.

Quality function deployment (QFD) has been widely applied in new product development, but existing research on QFD has some limitations. Primarily, QFD lacks the capability to provide feedback on the satisfaction degree of customer requirements (CRs) according to the actual values of engineering characteristics (ECs). In addition, QFD does not quantitatively consider the interrelationships among ECs. Reverse QFD (R-QFD) was introduced to implement the feedback process. On this basis, this paper quantitatively considers the interrelationships among ECs in the R-QFD model and extends these relationships to encompass combinations of multiple ECs, aiming to improve the inference accuracy of the model.

A nonlinear regression model was established between CRs and ECs, aiming to infer the satisfaction degree of CRs based on the implementation status of ECs. This model considers the interdependencies among ECs and extends the consideration of pairwise EC correlations from every two to every fifteen. Lingo Software is utilized to seek solutions for this program. To facilitate the implementation of the program, a directive to simplify the solution has been proposed.

The experimental results indicate that the interrelationships among ECs significantly affect the inference accuracy of the R-QFD model, thereby verifying the necessity of considering higher-order interrelationships among ECs within the R-QFD framework. Based on the results from data experiments, this paper also proposes research recommendations pertaining to ECs hierarchy for varying quantities of ECs.

The outcomes of this study have further refined the R-QFD model, addressing its limitations of ignoring the interrelationships among ECs. This transformation elevates the R-QFD model from a relatively simple linear model to a nonlinear model formed through modeling, thereby enhancing its accuracy and applicability. In practical terms, this study provides case support for the application of the R-QFD model in manufacturing industry.

The study examines how process/organizational innovation and R&D spending mediate the relationship between financial performance and the resource dependence theory in Fintech, providing insights into effective innovation strategies for achieving sustainable financial performance.

Data from 191 financial firms in Taiwan was collected from annual reports using the Taiwan Economic Journal (TEJ), a financial information provider. Content analysis was used to measure innovation activities and financial performance, with process and organizational innovation defined. R&D expenditures were also collected and used in statistical analysis to explore the relationship between variables.

This study on the financial services industry shows that process innovation and R&D expenditure positively impact firm performance, while organizational innovation may have a negative short-term effect but could have long-term benefits.

Limitations of this study include vulnerability to spurious effects and the use of data from only listed financial service firms. Future research should use more short-term performance data and include unlisted firms in the financial services industry to extend the study’s coverage.

This study extends resource dependence theory to financial services and explores the effects of process and organizational innovation on firm performance. Results show that internal process management boosts performance, while external collaboration with startups enhances Fintech innovation and efficiency, with positive short-term effects. The study highlights the importance of interacting with external organizations to access resources and improve performance in financial services.

In public health emergencies, seeking confirmed cases’ activity trajectory information (CCATI) is crucial to the public’s efforts to combat the epidemic. The public can stabilize their sentiments and mitigate the risk of cross-infection by obtaining CCATI. We investigated the factors influencing users' intentions to seek CCATI to enhance the government’s risk communication capabilities and improve information platform services.

We analyzed how information ecological factors affect the intention to seek CCATI through perceived value. Data was collected from 429 Chinese citizens during the fourth wave of the coronavirus disease 2019 (COVID-19) pandemic. We used the structural equation model technology and bootstrap mediation effect test to examine the model.

Information understandability, information relevance, perceived severity and perceived vulnerability directly and positively affect the intention of seeking CCATI. While, the above relationships are also partially mediated by emotional value and functional value. Social support directly and negatively affects the intention of seeking CCATI, while the relationship is also partially mediated by emotional value and functional value. Curiosity directly and positively affects the intention of seeking CCATI, while the relationship is also partially mediated by emotional value. The relationship between the quality of the search service and the intention of seeking CCATI is not significant, instead, it is fully mediated by functional value. The influence effect of information relevance on the intention of seeking CCATI is the greatest, followed by perceived vulnerability. The mediating effect of functional value is higher than emotional value.

The findings may help governments enhance their risk communication capabilities and improve epidemic prevention and control measures, enhancing the appeal of information platforms.

We focused on CCATI, an area with limited scholarly attention. We analyzed CCATI-seeking factors using an information ecology theory, introducing perceived value as a mediator, thus offering novel perspectives and models.

In this study, suggestions on the protection and development of marine resources, sea area use and management, improvement of laws and regulations, monitoring of marine resources and impact assessment of marine environment are put forward.

Literature research method.

A major factor contributing to the decline in fishery resources is excessive fishing. At present, there are many problems to be solved in the exploitation and utilization of marine mineral resources in China. The pollution problem of marine tourism resources is becoming increasingly serious. Overmining of coastal sea resources has led to planning and management failures.

This paper discusses the current situation of marine resource exploitation and protection in China and analyzes the reasons for excessive resource exploitation from three aspects of marine concept, laws and regulations and marine management.

Online health communities (OHCs) have emerged on the Internet, substantially changing the conventional healthcare delivery model. Despite this emergence, the lack of patient participation and contribution always limits the success and sustainability of OHCs. Previous studies have disclosed that patients’ value co-creation behavior (VCB) helps organizations sustain OHCs. However, how the recent surge in artificial intelligence (AI) tools, such as social support chatbots (SSCs), drives patients’ VCB is still unknown. Therefore, this study examines the complex mechanism behind patients’ VCB to establish sustainable OHCs.

Using value co-creation and social support theories, the author develops a moderated mediation model and analyzes survey data from 338 respondents using partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) methods.

Results demonstrate that perceived social support (PSS) from SSCs positively affects VCB directly and indirectly via patient learning (PL). This indirect effect is stronger when patient ability/readiness (PAR) is high. ANN findings highlight the model’s robustness and the significant role of PAR in VCB.

This study’s integrated framework offers unique insights into key drivers of patients’ VCB in OHCs. The findings indicate that PSS from SSCs enhances PL and VCB, with PAR influencing the strength of these relationships. Understanding these dynamics can inform user-centric interventions to promote effective learning and collaboration in OHCs.

This study empirically examined four value attributes (e.g. conditional value, functional value, epistemic value and price consciousness) influencing young customers’ attitudes, word-of-mouth (WOM) and continuous usage intentions. Subjective norm was positioned as the moderator, while WOM was identified as the mediator.

This study collected data from 252 Chinese young customers using purposive sampling technique and utilized PLS-SEM to examine the interrelationships among variables.

The findings confirmed that the four value attributes (i.e. conditional value, functional value, epistemic value and price consciousness) significantly influence young consumers' attitudes toward pre-made dishes (PMDs). Additionally, attitudes and WOM positively influence continuous usage intention, with WOM acting as a mediator between attitudes and continuous usage intentions. Furthermore, subjective norm partially moderates the value-attitude-behavior (VAB) model.

PMDs manufacturers should take into account the consumption values and price consciousness of young customers when developing marketing campaigns. Subjective norm and WOM continue to be key factors in enhancing continuous intention.

This study expands the applicability of the VAB model, the theory of consumption value (TCV), and the theory of planned behavior (TPB), enriching the literature on PMDs by examining four value attributes and moderating factors influencing continuous usage intention.

Integrating the Chat Generative Pre-Trained Transformer-type (ChatGPT-type) model with government services has great development prospects. Applying this model improves service efficiency but has certain risks, thus having a dual impact on the public. For a responsible and democratic government, it is necessary to fully understand the factors influencing public acceptance and their causal relationships to truly encourage the public to accept and use government ChatGPT-type services.

This study used the Latent Dirichlet allocation (LDA) model to analyze comment texts and summarize 15 factors that affect public acceptance. Multiple-related matrices were established using the grey decision-making trial and evaluation laboratory (grey-DEMATEL) method to reveal causal relationships among factors. From the two opposite extraction rules of result priority and cause priority, the authors obtained an antagonistic topological model with comprehensive influence values using the total adversarial interpretive structure model (TAISM).

Fifteen factors were categorized in terms of cause and effect, and the antagonistic topological model with comprehensive influence values was also analyzed. The analysis showed that perceived risk, trust and meeting demand were the three most critical factors of public acceptance. Meanwhile, perceived risk and trust directly affected public acceptance and were affected by other factors. Supervision and accountability had the highest driving power and acted as the causal factor to influence other factors.

This study identified the factors affecting public acceptance of integrating the ChatGPT-type model with government services. It analyzed the relationship between the factors to provide a reference for decision-makers. This study introduced TAISM to form the LDA-grey-DEMATEL-TAISM method to provide an analytical paradigm for studying similar influencing factors.

The sudden COVID-19 epidemic in 2019 has frustrated China's overall economy, and the implementation and development of the National Fitness Program has encountered huge obstacles. At a new historical starting point, in order to realize the dream of becoming a powerful country in sports, it is necessary to transform the successful experience gained since the reform and opening up into regular understanding and systematic theories, so as to make a theoretical response to the new contradictions and challenges faced in development and give full play to the National Fitness has comprehensive values and multiple functions in improving people's health, promoting people's all-round development, promoting economic and social development and demonstrating the country's cultural soft power.

Taking the topic of national fitness as an example, this paper sets out from the three dimensions of knowledge input, knowledge output and knowledge production, using citation analysis, social network analysis, co-word analysis and cluster analysis, to measure the characteristics and knowledge structure of interdisciplinary knowledge exchange.

China's national fitness is still in the primary development stage, and the strong boost of the national top-level policy is the biggest driving force of its development, driven by the policy together with the settlement of many major events, constantly improving and enriching the wings. The main body of knowledge production on the topic of national fitness is mainly colleges and universities, with low participation of government and enterprises, high degree of cooperation among authors, obvious interdisciplinary characteristics and strong application of research themes.

This study provides a strong theoretical basis for the promotion of the Healthy China strategy. Especially under the influence of COVID-19, this paper can contribute to the comprehensive value and multimodal functions of national fitness in improving the health of people, promoting economic and social development and demonstrating the soft power of national culture.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.