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China’s rural elderly care system faces significant challenges in addressing the needs of an ageing population, particularly in resource-limited areas. This study aims to critically examine the rural mutual aid elderly care model in China through the lens of active ageing principles. It explores how this model tackles elder care challenges in rural areas by emphasising health, participation and security, aiming to assess its effectiveness in reducing care costs while fostering self-reliance and mutual assistance.

A qualitative research methodology was used, focusing on document analysis to explore the rural mutual aid elderly care model within an active ageing framework. The study reviewed policy documents, academic literature and organisational records, using thematic analysis to understand the historical context, policy evolution and institutional support for this model.

The research identifies a significant shift from traditional passive care models to those grounded in active ageing principles. The rural mutual aid model, centring on health, participation and security, enables elderly individuals to engage in self-help and mutual assistance, thereby enhancing autonomy and reducing dependency. Key success factors include strong community networks and culturally resonant support systems. However, challenges such as resource limitations and regional disparities suggest areas for further improvement.

The reliance on document analysis may not fully capture the experiences of elderly participants or account for regional variations in the model’s implementation. Future research could incorporate mixed methods, including interviews or case studies, for a more nuanced understanding of elderly care in rural China. The findings emphasise the need for scalable and adaptable policies to enhance the model’s effectiveness and support culturally appropriate care strategies.

The research presents significant implications for policymakers and practitioners regarding the rural mutual aid elderly care model. It emphasises the need for scalable policies that address resource limitations and enhance community engagement to improve care effectiveness. Key implications include promoting financial and social sustainability by fostering self-reliance among older adults, prioritising active ageing initiatives to enhance quality of life and community cohesion and adopting localised strategies that consider regional disparities. Moreover, these findings offer a blueprint for other countries facing similar ageing challenges, advocating for a culturally sensitive and community-driven approach to elderly care across East Asia.

The study’s findings significantly impact how ageing is perceived and managed in rural Chinese communities. The shift from traditional, passive elderly care models to those grounded in active ageing principles reflects a societal transformation in valuing and supporting older adults. By promoting health, participation and security, the rural mutual aid elderly care model creates inclusive environments where the elderly actively contribute to their well-being and that of others. This approach challenges perceptions of ageing as a decline, fostering autonomy and meaningful engagement, ultimately reducing social isolation and enhancing cohesion through strengthened intergenerational relationships and shared responsibilities.

This study contributes to the literature by focusing on the rural mutual aid elderly care model in China through active ageing principles, addressing a notable gap in the non-Western application of these concepts. The findings offer valuable recommendations for policymakers and practitioners, emphasising community-driven approaches to ageing challenges in rural areas.

The purpose of this paper is to test and explain the context where motivation to learn (MTL) reduces innovative behavior in the organizational context.

The authors used questionnaire survey to collect data in a field study. In order to test the moderating effect of transfer climate, MTL on the relationship between MTL and innovative behavior, a sample of 606 employees was analyzed to examine the theoretical expectation by using multiple regression and bootstrapping.

The authors found employees motivated to learn showed less innovative behavior when perceived transfer climate is less favorable. The authors further revealed that motivation to transfer mediates the moderating effect of transfer climate for the relationship between MTL and innovative behavior.

One suggestion for further research is to investigate the relationship among the four constructs by using multi-source, multi-wave and multi-level method.

This study provides several useful guidance of how organization and manager avoid the negative effects of MTL through encouraging employees to learn new knowledge and skills, and providing employee opportunities to use their acquired knowledge and skills.

The authors contribute to the motivational literature by taking a step further to understand the effect of MTL. The authors propose and confirm that employee MTL can lead to negative outcomes when individuals perceived transfer climate is low. The results offer new insight beyond previous findings on positive or non-significant relationship between MTL and innovative behavior. The results further show that this interactive effect is induced by motivation to transfer. Particularly, low transfer climate reduces individuals’ motivation to transfer, and individuals with high MTL have low innovative behavior when they are less motivated to transfer.

Denoising of the vibration signal is crucial to identify a structure's damage. Based on noise frequency character, the “real” vibration signal can be gotten. The purpose of this paper is to propose a novel method for denoising a signal based on the wavelet transform.

The vibration signal with noise which can be collected by wireless network is decomposed by wavelet transform. In order to select optimal level of wavelet decomposition, based on noise's frequency, power spectral density is used. A soft thresholding method based on minimum mean‐variance is used for vibration signal de‐noising with Gaussian noise.

A novel method has been described in his paper. Based on the relationship between vibration signal's character and noise frequency, the way to get rid of noise is combined wavelet transform with power spectral density.

In order to select optimal level of wavelet decomposition, based on noise's frequency, power spectral density is used. A soft thresholding method based on minimum mean‐variance is used for vibration signal denoising with Gaussian noise.