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This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

This paper aims to improve the refractive index sensor performance for analytes with large refractive index by adopting the technology of microstructured fiber (MF) and surface plasmon resonance (SPR).

The structure adopts an MF with a hexagonal lattice cladding structure composed of all-circular air holes, and three defect regions are introduced. The liquid analyte that needs to be tested is filled in the defect area. The surface plasmon polarition mode is generated and coupled with the core mode, thus forming a refractive index sensing channel. When the resonance conditions are satisfied, the resonance wavelength will be changed with the refractive index of the liquid analyte. All parameters that may affect the performance of the sensor are numerical simulated, and the structure is optimized through a large number of calculations.

The results demonstrate that the maximum dynamic sensitivity (S_R) can reach to 24,260 nm/RIU, and the average sensitivity (S_R-AV) can reach to 18,046 nm/RIU when the refractive index range is from 1.42 to 1.47. Besides, the sensitivity linearity (R²) is approximately 0.965, and its resolution is 4.1 × 10^–6 RIU. The comparison with some literature results shown that the proposed sensor has certain advantages over the sensors reported in these literatures.

This work proposed an SPR-based refractive index sensor with a simple MF structure. It has a certain reference significance for the design and optimization of SPR-based MF sensors. Moreover, owing to its simple structure, high refractive index sensitivity and linear sensing performance, this sensor will play an important role in the detection of high refractive index liquid analytes.

This paper aims to try to develop new, environmentally friendly and efficient lubricating additives; study the compatibility of carbon-based additives with different base oils [Polyalphaolefin (PAO)-3, PAO-20 and NPE-2]; and explore the lubrication mechanism.

Oleylamine modified carbon nanoparticles (CNPs-OA) were prepared and the dispersion stability of CNPs-OA in PAO-3, PAO-20 and NPE-2 base oils was investigated by transmission electron microscopy, Fourier transform infrared, thermogravimetric analysis, energy dispersive spectroscopy and X-ray photoelectron spectroscopy. Universal Mechanical Tester (UMT) platform was used to carry out experiments on the effects of different additive concentrations on the lubricating properties of base oil.

The mean friction coefficient of PAO-3, PAO-20 and NPE-2 reduced by 32.8%, 10.1% and 11.4% when the adding concentration of CNPs-OA was 1.5, 2.0 and 0.5 Wt.%, respectively. Generally, The CNPs-OA exhibited the best friction-reducing and anti-wear performance in PAO-3.

The agglomeration phenomenon of carbon nanoparticles as lubricating additive was improved by surface modification, and the lubricating effect of carbon nanoparticles in three synthetic aviation lubricating base oils was compared.

Corrosion is a major concern for many industries that use metals as structural or functional materials, and the use of corrosion inhibitors is a widely accepted strategy to protect metals from deterioration in corrosive environments. Moreover, the toxic nature, non-biodegradability and price of most conventional corrosion inhibitors have encouraged the application of greener and more sustainable options, with natural and synthetic drugs being major actors. Hence, this paper aims to stress the capability of natural and synthetic drugs as manageable and sustainable, environmentally friendly solutions to the problem of metal corrosion.

In this review, the recent developments in the use of natural and synthetic drugs as corrosion inhibitors are explored in detail to highlight the key advancements and drawbacks towards the advantageous utilization of drugs as corrosion inhibitors.

Corrosion is a critical issue in numerous modern applications, and conventional strategies of corrosion inhibition include the use of toxic and environmentally harmful chemicals. As greener alternatives, natural compounds like plant extracts, essential oils and biopolymers, as well as synthetic drugs, are highlighted in this review. In addition, the advantages and disadvantages of these compounds, as well as their effectiveness in preventing corrosion, are discussed in the review.

This survey stresses on the most recent abilities of natural and synthetic drugs as viable and sustainable, environmentally friendly solutions to the problem of metal corrosion, thus expanding the general knowledge of green corrosion inhibitors.

The objective of this research is to methodically categorize the various types of Open Government Data (OGD) stakeholders, and to elucidate the intricate network relationships among OGD stakeholders, along with the underlying mechanisms that shape their formation.

To comprehend the collaboration mechanism of stakeholders in the OGD ecosystem, the authors constructed an OGD multi-stakeholder relationship network by using data from the Shandong Province Data Application Innovation and Entrepreneurship Competition. Based on the structural social capital theory and exponential random graph model (ERGM), an analytical framework was established to explore the formation mechanism of the collaborative network of OGD multi-stakeholder.

The results indicate that multi-stakeholder collaboration among government, enterprises and the public is crucial for achieving OGD goals. Organizing OGD competitions serves as an effective mechanism for solidifying and maintaining relationships among OGD stakeholder groups. Degree centrality and structural parameters reveal a Matthew effect within the connection process of the OGD ecosystem's collaborative network. Additionally, there is evidence of agglomeration and transferability within the network's structure.

This study contributes to the understanding regarding the formation mechanism of OGD stakeholders. The findings have implications for developing multi-stakeholder relationship networks of OGD and driving OGD initiatives.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2023-0284

We investigate herding behavior and explore how risk aversion interacts with herding in a sample of selected sustainability indices. Furthermore, we evaluate volatility co-movements and dynamic and time-varying correlations of two notable indicators: the cross-sectional absolute deviation of returns (CSAD) and the risk aversion index (RAI). Moreover, we explore a spillover mechanism (in the short and long run) of risk aversion to sustainable investing.

Our study uses daily prices from 01/06/2012 to 15/07/2022 obtained from S&P Dow Jones Indices. We use autoregressive - Glosten, Jagannathan, and Runkle generalized autoregressive conditional heteroscedastic (AR(1)-GJR GARCH) methodologies to measure the impact of cross-sectional absolute deviation of returns (CSAD) and risk aversion (RAI) indicators on the conditional variance of selected sustainability indices. Furthermore, we employ dynamic conditional correlation with generalized autoregressive conditional heteroscedasticity, generalized autoregressive conditional heteroscedasticity generalized autoregressive conditional heteroscedasticity mixed data sampling and dynamic conditional correlation with mixed data sampling models to examine any dynamic correlation, spillover volatility effects and the predictability stance of the CSAD and the RAI factors on sustainable investing.

Our empirical outcomes capture no-herding behavior but display herding on the risk aversion indicator. The cross-sectional dispersion of returns positively affects the conditional variance of all selected sustainable markets, besides emerging markets, while the risk aversion factor negatively influences the conditional variance for Europe and the USA. We have identified long-term contagion effects relating to the variability of returns in our sample, with the exception of emerging markets.

The dispersion of returns can predict the swings of long-term volatilities of Nordic and Europe markets, while the risk aversion factor can predict the long-run volatilities of sustainable markets except Nordic.

The current study presents, for the first time in the sustainable finance literature, an empirical analysis of herding and risk aversion in sustainable investment returns over time. Our findings offer valuable insights to fund managers, finance professionals and investors, providing them with an opportunity to proactively manage their portfolios and reduce financial risk by understanding the volatility behavior of sustainable investing. Furthermore, it is crucial to explore and understand how the dispersion of returns and risk aversion interact with sustainable markets for the construction of optimal portfolios.

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.

Social media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. This paper aims to sort out the depression-related study conducted on the text on social media, with particular attention to the research theme and methods.

The authors finally selected research articles published in Web of Science, Wiley, ACM Digital Library, EBSCO, IEEE Xplore and JMIR databases, covering 57 articles.

(1) According to the coding results, Depression Prediction and Linguistic Characteristics and Information Behavior are the two most popular themes. The theme of Patient Needs has progressed over the past few years. Still, there is a lesser focus on Stigma and Antidepressants. (2) Researchers prefer quantitative methods such as machine learning and statistical analysis to qualitative ones. (4) According to the analysis of the data collection platforms, more researchers used comprehensive social media sites like Reddit and Facebook than depression-specific communities like Sunforum and Alonelylife.

The authors recommend employing machine learning and statistical analysis to explore factors related to Stigmatization and Antidepressants thoroughly. Additionally, conducting mixed-methods studies incorporating data from diverse sources would be valuable. Such approaches would provide insights beneficial to policymakers and pharmaceutical companies seeking a comprehensive understanding of depression.

This article signifies a pioneering effort in systematically gathering and examining the themes and methodologies within the intersection of health-related texts on social media and depression.

Considering behavior-based pricing strategy, we explore the choice of instant logistics service model and pricing strategy of Quick-commerce channel.

We adopt the Hotelling framework to develop a two-period game in which two horizontally differentiated suppliers sell repetitively purchased products through a traditional e-commerce channel and an instant e-commerce channel in two periods.

The results show that: (1) when consumer’s mismatch cost and instant logistics cost coefficient are moderate or relatively high or low, platform-operated logistics (PL) is more beneficial to traditional supplier and traditional e-platform, while Q-commerce platform may prefer self-operated logistics. However, for Q-commerce suppliers, as his/her instant logistics cost coefficient increases, he/she tends to prefer PL. (2) If the instant logistics cost coefficient is moderate, traditional suppliers may always earn more than Q-commerce suppliers in both models, despite the higher commission rate of traditional e-platform. (3) When the instant logistics cost coefficient for Q-commerce suppliers is low, traditional suppliers should significantly reduce price for new customers under PL.

Our research constructs a competition between traditional and quick commerce channels, using game theory methods to examine the impact of different instant logistics models on the dynamic pricing strategies, profitability and instant logistics efficiency of these two online channels.

It is a complex and dynamic process to provide high-quality rural infrastructure. However, there lacks a holistic performance evaluation method for rural infrastructure provision that reflects changing rural social needs and takes a village as a whole. This study aims to develop a holistic and dynamic performance evaluation model for rural infrastructure in Mainland China.

This study established an evaluation index system by combining the lifecycle theory and the economy, efficiency, effectiveness and equity (4E) theory. This study developed an evaluation model by using the analytic network process (ANP) and matter-element analysis theory (MEAT). The model was validated by two representative villages in Mainland China.

The developed model can reflect dynamic social needs and effectively evaluate the overall infrastructure provision performance of a village. The weight of indicators reflects the changes in Mainland China’s contemporary rural social needs, with particular emphasis on the impact and output performance. The evaluation result shows that the overall performance of the representative villages was excellent but had a tendency toward good. Although the output performance was excellent, different input, process and impact performances resulted in different downgrade trends.

This study provides a theoretical basis for disaggregating the complex issue of the performance of rural infrastructure provision. The results can be used by relevant authorities to make a holistic and dynamic evaluation of the performance of rural infrastructure provision and timely revise planning and management policies.