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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.

As live streaming becomes a widely used online sales mode, previously content-centred anchors are attempting to switch to e-commerce live streaming. The purpose of this research was to explore the mechanisms that prompt consumers to stay or leave after content anchors transfer to live e-commerce broadcasts. In addition, we explored the factors affecting consumption from the perspectives of anchors, consumers and the external environment.

We distributed questionnaires to a group of fans who had experienced the transition of content anchors to live streaming and received back 375 valid questionnaires. Using psychological contract theory, we constructed a theoretical model for the scenario in which content anchors transition to live e-commerce broadcasting and analysed the data using partial least squares structural equation modelling (PLS-SEM).

The results show that circle culture, mainstream culture, initial trust and live streaming content all positively influenced consumers’ attitudes, whilst consumers’ past shopping experiences negatively influenced consumers’ attitudes. The personal charm of the content anchors did not have a significant effect on consumers’ attitudes. Additionally, we found that only anchors with a significant circle culture and good trust levels amongst fans were able to transition to live e-commerce streaming successfully.

This study extends the application of psychological contract theory to the field of e-commerce and describes the transformation of different types of psychological contracts. The paper’s conclusions provide a reference for decision-making and the implementation of transformation by content-based anchors to live streaming, helping them to coordinate their relationships with fans more effectively.

The study specifically focused on ethical leadership and help seeking during innovation and tested the moderating effect of happiness on the path from help seeking to innovation.

In this longitudinal study, 472 employees from an HR outsourcing company in China participated in the first survey. However, only 184 employees completed two surveys (1 month apart). We used path analysis to test all hypotheses through Mplus.

We found that ethical leadership, through help-seeking, is correlated to innovation. We also found that one’s happiness level is related to innovation, and when employees are chronically happy, the positive effect of help-seeking on innovation is stronger.

Working in an ethical environment, employees are comfortable acknowledging mistakes, hearing feedback and making corrections, which fosters continuous innovation. Conversely, without the influence from ethical leaders, employees may choose to remain silent and abandon continuous innovation due to the concern of losing “face” the context of China.

The purpose of this study is to evaluate three types of textures designed to enhance the tribological performance of static and dynamic pressure thrust bearings.

To explore the effects of different types of textures on tribological performance, the Reynolds equation is modified using lubrication theory and computational fluid dynamics methods while considering the influence of cavitation and turbulence on the physical field. In addition, the tribological performance is optimized through an improved selection algorithm based on Pareto envelope (PESA).

The results indicate that textured thrust bearings exhibit superior tribological performance compared to untextured ones. The circular texture outperforms other textures in terms of load-bearing and friction performance, with improvements of approximately 28.8% and 18.9%, respectively. In addition, the triangular texture exhibits the most significant temperature improvement, with a reduction of approximately 1.93%.

The study proposes three types of textures and evaluates the friction performance of thrust bearings by modifying the Reynolds equation. In addition, the optimal texture design is determined using an improved selection algorithm based on PESA.

The lubrication performance of static and dynamic pressure thrust bearings is improved by introducing texture on the sealing edge.

Through model building, meshing and boundary condition setting, the influence of square texture on oil film lubrication performance was simulated and analyzed, and an improved algorithm was applied to perform optimization of lubrication performance.

The findings of this study reveal that the optimum lubrication performance is attained when adjusting the parameters of the square texture to 0.12 mm, 0.1 mm, 1 mm and 34 mm. In such circumstances, the thrust bearing with square textures demonstrates an increase in loading capacity of around 19% and a temperature reduction of about 2ºC compared to a smooth thrust bearing.

The original Reynolds equation is revised, and the influence of square texture on the physical field of oil film is analyzed, considering the turbulence state and cavitation phenomenon. The multi-objective function under square texture parameters was established using BP neural network, and the improved multi-objective salp swarm algorithm was used to optimize the process parameters.

The purpose of this study is to explore the potential of trainable activation functions to enhance the performance of deep neural networks, specifically ResNet architectures, in the task of image classification. By introducing activation functions that adapt during training, the authors aim to determine whether such flexibility can lead to improved learning outcomes and generalization capabilities compared to static activation functions like ReLU. This research seeks to provide insights into how dynamic nonlinearities might influence deep learning models' efficiency and accuracy in handling complex image data sets.

This research integrates three novel trainable activation functions – CosLU, DELU and ReLUN – into various ResNet-n architectures, where “n” denotes the number of convolutional layers. Using CIFAR-10 and CIFAR-100 data sets, the authors conducted a comparative study to assess the impact of these functions on image classification accuracy. The approach included modifying the traditional ResNet models by replacing their static activation functions with the trainable variants, allowing for dynamic adaptation during training. The performance was evaluated based on accuracy metrics and loss profiles across different network depths.

The findings indicate that trainable activation functions, particularly CosLU, can significantly enhance the performance of deep learning models, outperforming the traditional ReLU in deeper network configurations on the CIFAR-10 data set. CosLU showed the highest improvement in accuracy, whereas DELU and ReLUN offered varying levels of performance enhancements. These functions also demonstrated potential in reducing overfitting and improving model generalization across more complex data sets like CIFAR-100, suggesting that the adaptability of activation functions plays a crucial role in the training dynamics of deep neural networks.

This study contributes to the field of deep learning by introducing and evaluating the impact of three novel trainable activation functions within widely used ResNet architectures. Unlike previous works that primarily focused on static activation functions, this research demonstrates that incorporating trainable nonlinearities can lead to significant improvements in model performance and adaptability. The introduction of CosLU, DELU and ReLUN provides a new pathway for enhancing the flexibility and efficiency of neural networks, potentially setting a new standard for future deep learning applications in image classification and beyond.

Based on the theoretical predictions of media equation theory and the computers-are-social-actors (CASA) perspective, this study aims to examine the effects of performance error type (i.e. logical, semantic or syntactic), task type and personality presentation (i.e. dominant/submissive and/or friendly/unfriendly) on users’ level of trust in their personal digital assistant (PDA), Siri.

An experimental study of human–PDA interactions was performed with two types of tasks (social vs functional) randomly assigned to participants (N = 163). While interacting with Siri in 15 task inquiries, the participants recorded Siri’s answers for each inquiry and self-rated their trust in the PDA. The answers were coded and rated by the researchers for personality presentation and error type.

Logical errors were the most detrimental to user trust. Users’ trust of Siri was significantly higher after functional tasks compared to social tasks when the effects of general usage (e.g. proficiency, length and frequency of usage) were controlled for. The perception of a friendly personality from Siri had an opposite effect on social and functional tasks in the perceived reliability dimension of trust and increased intensity of the presented personality reduced perceived reliability in functional tasks.

The research findings contradict predictions from media equation theory and the CASA perspective while contributing to a theoretical refinement of machine errors and their impact on user trust.

This study aims to discuss whether the lasting Confucian philosophy could be used in responding to the consequences of technological impacts on jobs in the Age of Robots from a human resource management and development (HRMD) perspective.

Related literature concerning traditional Confucian philosophy and the power of Confucianism was examined. Key perspectives on this topic relating to smart technology were analyzed. Whether Confucian humanity could be used to promote ethical behavior and continuous improvement in the workplace in the Age of Robots was then discussed.

Three propositions were made: humans can better coexist with artificial intelligence (AI) and robots if humanity is valued, cultivated and practiced; some concepts of traditional Confucian philosophy can be applied to support management, employees and organizations to go through the technology-driven social change; and managements and human resource professionals can be the change agent and adopt Confucian paradigm for employees’ and organizational effectiveness in the Age of Robots.

Future research on human–machine interactions and strategic plans to apply Confucian humanity on job restructuring in robotic workplace is recommended.

For organizational development implication, human resource professionals may identify business opportunities, develop human–machine interactions strategic plans, build out creative process and promote moral behaviors and ethical conduct with a growth mindset.

For corporate social responsibility, management and human resource professionals can upskill and reskill employees to develop talents, avoid technology unemployment and advance their human skills to be competitive in the robotic workplace.

This study highlighted how human workers should work like a human, not as a robot, by building a lifelong character through a moral refinement process for self-fulfillment, social responsibility and social stability.

The reported Kullback–Leibler (K–L) distance-based generalized grey target decision method (GGTDM) for mixed attributes is an asymmetric decision-making basis (DMB) that does not have the symmetric characteristic of distance in common sense, which may affect the decision-making result. To overcome the deficiency of the asymmetric K–L distance, the symmetric K–L distance is investigated to act as the DMB of GGTDM for mixed attributes.

The decision-making steps of the proposed approach are as follows: First, all mixed attribute values are transformed into binary connection numbers, and the target centre indices of all attributes are determined. Second, all the binary connection numbers (including the target centre indices) are divided into deterministic and uncertain terms and converted into two-tuple (determinacy and uncertainty) numbers. Third, the comprehensive weighted symmetric K–L distance can be computed, as can the alternative index of normalized two-tuple (deterministic degree and uncertainty degree) number and that of the target centre. Finally, the decision-making is made by the comprehensive weighted symmetric K–L distance according to the rule that the smaller the value, the better the alternative.

The case study verifies the proposed approach with its sufficient theoretical basis for decision-making and reflects the preferences of decision-makers to address the uncertainty of an uncertain number.

This work compares the single-direction-based K–L distance to the symmetric one and uses the symmetric K–L distance as the DMB of GGTDM. At the same time, different coefficients are assigned to an uncertain number’s deterministic term and uncertain term in the calculation process, as this reflects the preference of the decision-maker.

Marketing Technology (Martech) is the cornerstone of creating digital experiences and interactive marketing, providing consumers with high experiential value. Drawing on the mindfulness theory, this study aims to explore how to achieve close psychological distance and experiential value in Martech servicescape (such as augmented reality [AR]).

We employed mixed methods research to clarify the research question. In Study 1, we conducted a systematic literature review of psychological closeness (PC) using a bibliographic coupling approach, identifying gaps in the research stream and discussing the research implications for the interactive marketing field. In Study 2, we used a task-based laboratory assessment to empirically verify our hypotheses and research framework. Two virtual try-on environments, AR and non-AR (e.g. traditional webpage browsing), were applied in a virtual fitting context. The two e-shopping environments were directly compared in terms of their moderating effects on the relationships among the mindfulness-oriented MarTech servicescape, PC and experiential value.

This study elucidates the antecedent of close psychological distance formation, indicating that the features of the mindfulness-oriented Martech servicescape – vivid sensory experience, consumer-focused shopping information and autonomous navigation, then result in creating experiential value. Moreover, this study also revealed that compared to a non-AR e-shopping environment, AR makes the better effect of the mindfulness-oriented Martech servicescape driving experiential marketing.

This study extends the research stream on mindfulness-oriented service to the Martech servicescape (e.g. AR try-on). In this way, this study’s findings will contribute to clarifying the interactive elements and design principles of mindfulness-oriented service in the Martech servicescape. By establishing the association between these three theoretical perspectives—mindfulness-oriented service research stream, construal level theory and experience economy paradigm—the study provides valuable insights into how Martech can enhance experiential marketing. Such research insights can help digital marketing managers shape appropriate Martech servicescape for effective experiential marketing.