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The purpose of this study is to investigate the influence of rotational speed on the oil film stability of the hydrostatic rotary table having double rectangular oil pads. The oil film stability is evaluated based on the oil film stiffness under constant load condition and the displacement response amplitude of the oil film under disturbance load condition.

The oil film stability theoretical equations of the double rectangular oil cavity are deduced such as oil film stiffness, damping and dynamic equations. A simulation model is developed to analyze the relationship among oil film temperature, oil film pressure fields and oil film stability. The user-defined function programs are used to control the rotational speed, lubricant viscosity and oil film thickness during the simulation. In addition, an experimental rig is built to test the simulation results.

This study shows that oil film stability decreases with increasing rotational speed under constant load and disturbance load. The trend of oil film stability decreased slowly within 30 r/min, and then rapidly. However, since the hydrodynamic pressure effect, the decrease rate of stability is mitigated under constant load and high rotational speeds.

The conclusions can provide a theoretical basis for improving the oil film stability of machines with similar hydrostatic support structure.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0267/

This paper aims to study the influence of cylindrical texture parameters on the lubrication performance of static and dynamic pressure thrust bearings (hereinafter referred to as thrust bearings) and to optimize their lubrication performance using multiobjective optimization.

The influence of texture parameters on the lubrication performance of thrust bearings was studied based on the modified Reynolds equation. The objective functions are predicted through the BP neural network, and the texture parameters were optimized using the improved multiobjective ant lion algorithm (MOALA).

Compared with smooth surface, the introduction of texture can improve the lubrication properties. Under the optimization of the improved algorithm, when the texture diameter, depth, spacing and number are approximately 0.2 mm, 0.5 mm, 5 mm and 34, respectively, the loading capacity is increased by around 27.7% and the temperature is reduced by around 1.55°C.

This paper studies the effect of texture parameters on the lubrication properties of thrust bearings based on the modified Reynolds equation and performs multiobjective optimization through an improved MOALA.

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 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.

This paper aims to provide a framework of the supervised hyperspectral classification, to study the traditional flowchart of hyperspectral image (HIS) analysis and processing. HSI technology has been proposed for many years, and the applications of this technology were promoted by technical advancements.

First, the properties and current situation of hyperspectral technology are summarized. Then, this paper introduces a series of common classification approaches. In addition, a comparison of different classification approaches on real hyperspectral data is conducted. Finally, this survey presents a discussion on the classification results and points out the classification development tendency.

The core of this survey is to review of the state of the art of the classification for hyperspectral images, to study the performance and efficiency of certain implementation measures and to point out the challenges still exist.

The study categorized the supervised classification for hyperspectral images, demonstrated the comparisons among these methods and pointed out the challenges that still exist.

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.

As an effective tool to promote rational resource allocation and facilitate the development of green management practices such as enterprise digital innovation, the green credit policy has recently gained extensive attention. The purpose of this paper is to analyze the relationship between green credit policies and the digital innovation of enterprises, and to further explore the mechanism of action between them and their boundary conditions.

Based on micro-level data on Chinese firms from 2007 to 2019, this paper constructs a difference-in-differences (DID) model to investigate the impact and intrinsic mechanisms of green credit policies on firms' digital innovation and its boundary conditions, with the help of a quasi-natural experiment, i.e. the Green Credit Guidelines.

Green credit policies inhibit digital innovation and fail to compensate for innovation. The analysis of the mechanism shows that the implementation of green credit policies has a negative impact on digital innovation by increasing the financing constraints faced by firms, and has also a crowding-out effect on R&D investment, resulting in a disincentive to digital innovation. Further analysis reveals that the negative impact of green credit policies on digital innovation is more pronounced in state-owned enterprises, enterprises without financially experienced executives, and in the eastern regions of China.

This study provides empirical evidence to understand the effectiveness and mechanism of influence of green credit policies on enterprise digital innovation, providing also a basis to further improve green credit policies.

This paper aims to represent an empirical study of what role does structural empowerment play in the relationship between multi-stakeholder value co-creation (VCC) and the success of new product development (NPD).

The hypotheses developed in this paper are tested by using data from 243 manufacturing companies worldwide.

The results show that the VCC activities have significant positive effects on NPD success, and structural empowerment moderates the impact of VCC activities on NPD success, but the moderating effect is not significant for customer involvement.

This study contributes to the research of VCC and discusses the positive role of structural empowerment to enhance the impact of VCC activities on NPD success.

A proliferation of articles surrounding the COVID-19 pandemic is calling for new insights through review. This paper aims to bibliometrically analyze the current progress of research around hospitality and tourism to define the research directions on herd immunity and the prevention of disease under the “new normal.”

This paper analyzed 326 articles regarding COVID-19 published in SSCI hospitality, leisure and tourism journals in 2020 and 2021 by combining manual analysis and bibliometrics to reveal research topics and to gain insight into research structures.

The results of this paper summarized topics related to stakeholders’ mentality and behavior, responses of travel suppliers to the COVID-19 pandemic, economic impact and demand forecasting, social issues of human rights and racism and reflection on tourism and transformation of the industry. More research is called for in the future to focus on a better response to the crisis, including crisis management education and training and the improving the resilience of small- and medium-sized enterprises.

A three-dimensional consideration was proposed to promote the sustainable development of hospitality and tourism.

In the “new normal” phase of herd immunity and disease prevention, to the best of the authors’ knowledge, this is the first paper that provides an up-to-date systematic overview of the evolution of COVID-19 research in tourism and hospitality and encourages more conceptual, practical and futuristic studies.

The purpose of this study is to present a holistic review of journal articles on value cocreation in the tourism and hospitality field. By distinguishing similarities and differences in the contexts of China and other regions, the authors identify targeted research directions.

This study collected 438 English articles and 31 Chinese articles on value cocreation in tourism and hospitality published in the Web of Science, Scopus and China National Knowledge Infrastructure online databases. After analyzing the study location of all articles in the sample, the authors identified 79 English-language articles that adopted China as the context of the study. The first data set included non-China-focused publications and the second data set included China-focused publications. A comparative bibliometric methodology was conducted.

The results indicate similarities and differences between the two data sets in terms of research methods, topics and future research directions. Seven major concepts were identified in the first data set: hospitality value cocreation; the value generation process; key stakeholders; outcome variables; the application of service-dominant logic; peer-to-peer accommodation and destination management. Four research focuses were identified in the second data set: the application of customer-dominant logic; the value generation process; resource integration and the cocreation experience.

This research contributes to the understanding of tourism and hospitality cocreation systems through literature analysis and encourages future research on the nexus of the Chinese and global contexts. This systematic investigation extends the concept of value cocreation by integrating its implementation process and value categories and further evaluates the consequences and benefits of value cocreation, which helps clarify how winners and losers can adjust their strategies accordingly.

The originality of this paper lies in the adopted comparative bibliometric analysis which comprehensively reviewed and compared the characteristics of value cocreation research across two contexts. In addition, the study dialectically assesses Chinese tourism cocreation phenomena and their impacts from a global perspective.