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This study aims to develop a conceptual model to understand how customer knowledge management (CKM) affects fitness club membership renewal through the mediation of relationship quality.

Data were collected outside of fitness clubs using a systematic sampling method. A total of 224 valid responses were collected. Structural equation modelling was used to evaluate the relationship between the constructs of the research model.

The results indicate that both knowledge from customers and knowledge for customers have a positive influence on customer satisfaction and customer trust. Among them, knowledge for customers has a stronger influence on customer satisfaction while knowledge from customers has a greater influence on customer trust. Additionally, three dimensions of relationship quality (customer satisfaction, customer trust and customer commitment) positively influence membership renewal intention with customer commitment exhibiting the greatest influence on it.

This study combines the theories of CKM and relationship quality management to explain why members will renew their service contracts. By using fitness clubs as an example, this research extends the authors' understanding of how knowledge from and for customers can influence customers' attitudes and behavioural intentions towards service companies.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

Strategic flexibility (SF) has become increasingly important for firms because of the fast changes in the external environment. In line with the practical importance of SF, an emerging research field has developed around it that has attempted to understand the nature of SF and the key relationships. The aim of this study is to unveil the semantic structure of the recent literature on SF and to suggest new promising areas for future research.

The authors conduct a systematic literature review with a bibliographic analysis technique, which allows authors to identify the main recent streams in the literature, as well as offer reflections and suggestions for future research.

The authors uncover three main emerging areas in the research on SF, namely SF as a dynamic capability, the role of knowledge management for SF and the relationship between a firm SF and the external environment. The authors put forward three avenues for future research on SF: Avenue 1. SF, business model innovation (BMI) and other dynamic capabilities (DC), Avenue 2. Digital technologies and SF/organizational agility and Avenue 3. SF and sustainability. Articles included in the special issue entitled “A strategic perspective on flexibility, agility and adaptability in the digital era” contribute to Avenue 2, thus paving the way for filling some of the identified gaps regarding the relationship between SF and digitalization.

To the best of authors’ knowledge, this is the first literature review on SF that uses a bibliometric approach to draw conclusions on the findings in the literature. The review contributes to the theoretical understanding of SF by illustrating and explicating core topics that have persisted over time, as well as by presenting three main avenues for further developing authors’ knowledge around SF.

Interorganizational power dependence has become an increasingly important factor for small and medium-sized enterprises (SMEs) to improve product innovation. This paper examines the role of power dependence in SMEs' product innovation trade-offs between exploration and exploitation. The article further studies the mediating effect of supply chain adaptability and the moderating effect of knowledge acquisition on the relationship between power dependence and product innovation.

The study proposes a model to verify the impact of power dependence on SMEs' product innovation trade-offs based on social network theory. Two conceptually independent constructs, “availability of alternatives (ALTRN)” and “restraint in the use of power (RSPTW),” are used to evaluate the power dependence. The model also analyzed how these effects are mediated by supply chain adaptability and moderated by knowledge acquisition. The authors test these relationships using data collected from 224 SMEs in China.

The empirical analysis shows that ALTRN has a more substantial effect on exploration for product innovation, while RSTPW has a more significant impact on exploitation for product innovation. Moreover, empirical data indicate a partial mediating effect by supply chain adaptability between power dependence and product innovation of SMEs. The results also show that knowledge acquisition positively moderates the relationship between ALTRN/RSTPW, supply chain adaptability and product innovation.

Overall, the findings of the study advance the understanding of the roles of power dependence in product innovation for SMEs. In addition, the research also uncovers the impact mechanisms of existing theoretical frameworks and extends the boundaries of the theory.

To develop electrode materials for supercapacitor with superior electrochemical performance and simple preparation process, the purpose of this study is to prepare flexible CC/NiS/a-NiS electrodes with self-supporting structure by loading hydrothermally synthesized a-NiS particles along with nano-NiS on carbon cloth by electroplating method.

The effects of current densities, temperatures and pH values on the loading amount and uniformity of the active substances during the plating process were investigated on the basis of optimization of surface morphology, crystalline structure and electrochemical evaluation as the cyclic voltammetry curves, constant current charge–discharge curves and AC impedance.

The a-NiS particles on CC/NiS/a-NiS were mostly covered by the plated nano-NiS, which behaved as a bulge and provided a larger specific surface area. The CC/NiS/a-NiS electrode prepared with the optimized parameter exhibited a specific capacitance of 115.13 F/g at a current density of 1 A/g and a Coulomb efficiency of 84% at 5 A/g, which is superior to that of CC/NiS electrode prepared by electroplating at a current density of 10 mA/cm², a temperature of 55°C and a pH of 4, demonstrating its fast charge response of the electrode and potential application in wearable electronics.

This study provides an integrated solution for the development of specifically structured NiS-based electrode for supercapacitor with simple process, low cost and high electrochemical charge/discharge performance, and the simple and easy-to-use method is also applicable to other electrochemically active composites.

Abnormal vibrations often occur in the liquid oxygen kerosene transmission pipelines of rocket engines, which seriously threaten their safety. Improper handling can result in failed rocket launches and significant economic losses. Therefore, this paper aims to examine vibrations in transmission pipelines.

In this study, a three-dimensional high-pressure pipeline model composed of corrugated pipes, multi-section bent pipes, and other auxiliary structures was established. The fluid–solid coupling method was used to analyse vibration characteristics of the pipeline under various external excitations. The simulation results were visualised using MATLAB, and their validity was verified via a thermal test.

In this study, the vibration mechanism of a complex high-pressure pipeline was examined via a visualisation method. The results showed that the low-frequency vibration of the pipe was caused by fluid self-excited pressure pulsation, whereas the vibration of the engine system caused a high-frequency vibration of the pipeline. The excitation of external pressure pulses did not significantly affect the vibrations of the pipelines. The visualisation results indicated that the severe vibration position of the pipeline thermal test is mainly concentrated between the inlet and outlet and between the two bellows.

The results of this study aid in understanding the causes of abnormal vibrations in rocket engine pipelines.

The causes of different vibration frequencies in the complex pipelines of rocket engines and the propagation characteristics of external vibration excitation were obtained.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

Understandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations.

Analysis here is facilitated through network analysis together with topological and hierarchical characterizations of price comovements.

This study determines eight sectoral groups of cities whose house price indices are directly connected and the price synchronization within each group is higher than that at the national level, although each shows rather idiosyncratic patterns. Degrees of house price comovements are generally lower starting from 2018 at the national level and for the eight sectoral groups. Similarly, this study finds that the synchronization intensity associated with the house price index of each city generally switches to a lower level starting from early 2019.

Results here should be of use to policy design and analysis aiming at housing market evaluations and monitoring.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

This paper aims to identify the optimal forming angle for the selective laser melting (SLM) process and evaluate the mechanical properties of the SLM-formed GH3536 alloy in the aero-engine field.

Forming the samples with optimized parameters and analyzing the microstructure and properties of the block samples in different forming angles with scanning electron microscope, XRD, etc. so as to analyze and reveal the laws and mechanism of the block samples in different forming angles by SLM.

There are few cracks on the construction surface of SLM formed samples, and the microstructure shows columnar subgrains and cellular subgrains. The segregation of metal elements was not observed in the microstructure. The pattern shows strong texture strength on the (111) crystal plane. In the sample, the tensile strength of 60° sample is the highest, the plasticity of 90° forming sample is the best, the comprehensive property of 45° sample is the best and the fracture mode is plastic fracture. The comprehensive performance of the part is the best under the forming angle of 45°. To ensure the part size, performance and support structure processing, additional dimensions are added to the part structure.

In this paper, how to make samples with different forming angles is described. Combined with the standard of forged GH3536 alloy, the microstructure and properties of the samples are analyzed, and the optimal forming angle is obtained.