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Family planning (FP) services through the lens of human rights are not well known in Nepal. This study aims to assess Family Planning 2020: Rights and Empowerment Principles for Family Planning and identify factors affecting contraceptive use among HIV-infected women living in rural Far Western Nepal.

This study conducted a cross-sectional survey using self-designed proforma. To assess the association between contraceptive use and independent variables, this study calculated adjusted odd ratio (AOR) with 95% confidence interval (CI) using statistical package for social sciences (SPSS) V.20.

Only 37.8% of participants had access to full range of contraceptive methods, and only 57.5% of participants received proper counseling. Agency/autonomy, transparency/accountability and voice/participation were practiced by 43.7%, 23.4% and 19.7% of participants, respectively. Husband’s support (AOR = 4.263; 95% CI: 1.640–11.086), availability of FP services in their locality (AOR = 2.497; 95% CI: 1.311–4.754), employment (AOR = 3.499; 95% CI: 1.186–10.328) and postpartum period (AOR = 0.103; 95% CI: 0.023–0.475) were significantly associated with contraceptive use.

Health-care providers’ and program managers’ perspectives were not examined.

Findings of this study will be useful for making strategic plan on human rights-based approach to FP.

Expanding access to contraceptive information and services and strengthening autonomy, accountability and participation are key to human rights-based approach to FP.

This study identified that inadequate counselling, nonavailability of full range of contraceptive methods, low level of autonomy, accountability and participation were key bottlenecks in fulfilling human rights-based approach to FP.

The purpose of this study is to investigate the impact of corporate social responsibility (CSR) on innovation performance and examine the moderating role of social media strategic capability and big data analytics capability. Specifically, the authors explore the effects of both external and internal CSR on innovation performance.

The authors collected data from 221 senior, middle and research and development (R&D) managers of high-tech firms in China, using a questionnaire survey with a six-month interval.

The empirical results show that both external and internal CSR positively influence innovation performance. Furthermore, social media strategic capability has a positive moderating effect on the relationship between CSR and innovation performance, while big data analytics capability moderates the relationship between external CSR and innovation performance.

The data comes from high-tech firms in China, which may limit the generalizability and external validity of the findings. Future studies should replicate this study in other industries and types of organizations.

The study suggests that high-tech firms should engage in both external and internal CSR activities to promote innovation performance. Moreover, leveraging social media strategic capability and big data analytics capability can enhance innovation performance.

This study contributes to the literature on CSR outcomes by empirically exploring the effects of external and internal CSR on innovation performance, thus extending stakeholder theory. Additionally, by revealing the contingency effects of social media strategic capability and big data analytics capability, this study enriching the research on dynamic capabilities theory in the context of digital transformation.

This article aims to explore the key role of community participation in the protection and development of national parks under the global trend of national park development and provide reference for the construction of China’s national park system by analyzing international successful cases and experiences.

The study on “International Experience of Community Support for National Park Development” integrates multimethods, from data mining reports, journals and policy docs from WB, UNEP, to case analyses. In-depth interviews with policymakers, academics and farmers reveal needs, challenges and best practices. Comparative analysis tailors findings to China’s context, offering recommendations for enhancing community support. This hybrid approach ensures practical insights for China’s application.

The results of this study underscore the paramount significance of community participation as a cornerstone in advancing sustainable development and safeguarding national parks amidst a growing global awareness of environmental stewardship. Through a thorough examination of international National Parks such as Yellowstone, Maasai Mara, Uluru-Kata Tjuta and Sanjiangyuan, we reveal a blueprint of success that hinges on robust policy support, empowerment of local communities, strategic economic incentives and multifaceted cross-sectoral collaborations. In the context of domestic hurdles, including inadequate legal frameworks, narrow participation avenues and resource scarcities, our analysis outlines actionable recommendations aimed at fortifying policy and legal frameworks, establishing efficient engagement modalities, bolstering community capacity-building initiatives and fostering economic sustainability. This comprehensive approach presents a visionary roadmap for World’s national park system, guiding it towards achieving an optimal equilibrium where ecological integrity and community prosperity coexist harmoniously.

The article underscores the originality in illuminating the pivotal role of community participation in national park protection and development amidst a global shift. By delving into international exemplars like Yellowstone, Maasai Mara, Uluru-Kata Tjuta, Sanjiangyuan and Panda, it uncovers novel insights on policy frameworks, community empowerment, economic incentives and collaborative models. This work contributes to the burgeoning discourse on balancing ecological conservation with socioeconomic development, providing a blueprint for sustainable national park management of all the world.

The study aims to examine how different types of green brand ritual (GBR) influence customer engagement behavior and the mediation mechanisms and boundary conditions of the positive and negative pathways.

The study conducts two online experiments to collect data from a total of 940 consumers in China. Hypotheses are tested by independent samples t-test, two-way ANOVA and Hayes' PROCESS model.

Different kinds of GBR have different effects on customer engagement behavior. Internal GBR is more likely to play a positive role by inciting connectedness to nature. External GBR is more likely to play a negative role by inciting psychological resistance. This dual effect is especially pronounced for warm brands rather than competent brands.

The study pioneers the brand ritual into the field of interactive marketing and enriches its dual effect research. Additionally, the study figures out whether the category of brand ritual can trigger negative effect.

Inappropriate brand rituals are worse than no rituals at all. The results provide guidance for green companies to design effective brand rituals to strengthen the connection with consumers. Green brands should describe brand rituals in vivid detail and consciously lead consumers to immerse themselves in them.

In social media context, service failures become public domain, making them visible to vast audiences of customers who are virtually present. Thus, this study aims to discuss the roles of service failure type and management response on observers’ perceived helpfulness.

The authors conducted econometric analyses on a dataset incorporating 107,984 reviews and 34,641 management responses to negative reviews.

The results reveal that, for process failures, specifying a form of action (initiatives for solving the problem) is associated with more perceived helpfulness of reviews than accounts (explanation) or acknowledgments (recognition and acceptance), while responding to an outcome failure by providing an account is associated with increased perceived helpfulness of reviews.

For process failures, managers should make every effort to convince observers through specific actions that similar failures are less likely to occur in the future. For outcome failures, managers should strive to provide clear and fast explanations of the failure causes to restore observers’ evaluation of the firm’s capabilities.

The authors’ work extends sparse insights on observers and sheds new light on the effect of service failure type and response strategy on observers’ actual behaviors. The interplay between service failure type and response strategy provides guidance on how to use management responses to influence potential customers.

Migrant workers play a vital role in the development of countries such as China. However, they often face greater barriers in entrepreneurship. Yet, few studies have focused on the effect of institutional factors on migrant workers' entrepreneurial behaviors. To address this gap, this study concentrates on the role of taxation.

This study takes the agricultural tax abolition reform implemented in rural China since 2004 as an exogenous shock and uses the difference-in-difference (DID) method to examine its effect on migrant workers' entrepreneurship based on longitudinal data from the China Health and Nutrition Survey (CHNS).

We find that the reform significantly improves the probability of migrant workers engaging in entrepreneurship. Heterogeneity analysis reveals that the reform exerts a notably more pronounced positive impact on male, younger and migrant workers in the Western region. Further analysis reveals that the three main mechanisms are increased household income, female human capital accumulation and increased social capital, including social networks and social trust.

This study is first to offers an institutional perspective on the determinants of migrant workers' entrepreneurship in China. Additionally, the study can provide insights for policymaking intended to support employment and entrepreneurship among low-skill migrant workers.

In traditional Chinese medicine (TCM), the mechanism of disease (MD) constitutes an essential element of syndrome differentiation and treatment, elucidating the mechanisms underlying the occurrence, progression, alterations and outcomes of diseases. However, there is a dearth of research in the field of intelligent diagnosis concerning the analysis of MD.

In this paper, we propose a supervised Latent Dirichlet Allocation (LDA) topic model, termed MD-LDA, which elucidates the process of MDs identification. We leverage the label information inherent in the data as prior knowledge and incorporate it into the model’s training. Additionally, we devise two parallel parameter estimation algorithms for efficient training. Furthermore, we introduce a benchmark MD identification dataset, named TMD, for training MD-LDA. Finally, we validate the performance of MD-LDA through comprehensive experiments.

The results show that MD-LDA is effective and efficient. Moreover, MD-LDA outperforms the state-of-the-art topic models on perplexity, Kullback–Leibler (KL) and classification performance.

The proposed MD-LDA can be applied for the MD discovery and analysis of TCM clinical diagnosis, so as to improve the interpretability and reliability of intelligent diagnosis and treatment.

The purpose of this paper was to compare the electrochemical homogeneity of AZ91D after various heat treatment processes, and its influence on the growth, composition, microstructure and corrosion resistance of phosphate conversion coatings.

The electrochemical activity of different heat-treated Mg alloys was evaluated via scanning vibrational electrode technique; the characterization of the microstructure and phase composition of coatings was conducted using a scanning electron microscope and X-ray diffraction. The corrosion resistance was evaluated by electrochemical tests and accelerated neutral salt spray tests.

T6 treatment increased the electrochemical homogeneity, while T4 treatment decreased the microstructure homogeneity of AZ91D magnesium alloy, due to the existence of residual Al-Mn impurity phase. The phosphate conversion coating (PCC) on T6 heat-treated Mg alloys showed the most compact microstructure and the best corrosion resistance, while the coating on the T4 heat-treated Mg alloy exhibited the worst microstructure and corrosion resistance.

The microstructure and protectiveness of coatings are related to the homogeneousness of Mg alloy: an Mg substrate with a more heterogeneous electrochemical reactivity yields a PCC with less protectiveness, which could be explained by the difference of precipitation kinetics at the metal/electrolyte interface.

High-speed turnouts are more complex in structure and thus may cause abnormal vibration of high-speed train car body, affecting driving safety and passenger riding experience. Therefore, it is necessary to analyze the data characteristics of continuous hunting of high-speed trains passing through turnouts and propose a diagnostic method for engineering applications.

First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to determine the first characteristic component of the car body’s lateral acceleration. Then, the Short-Time Fourier Transform (STFT) is performed to calculate the marginal spectra. Finally, the presence of a continuous hunting problem is determined based on the results of the comparison calculations and diagnostic thresholds. To improve computational efficiency, permutation entropy (PE) is used as a fast indicator to identify turnouts with potential problems.

Under continuous hunting conditions, the PE is less than 0.90; the ratio of the maximum peak value of the signal component to the original signal peak value exceeded 0.7, and there is an energy band in the STFT time-frequency map, which corresponds to a frequency distribution range of 1–2 Hz.

The research results have revealed the lateral vibration characteristics of the high-speed train’s car body during continuous hunting when passing through turnouts. On this basis, an effective diagnostic method has been proposed. With a focus on practical engineering applications, a rapid screening index for identifying potential issues has been proposed, significantly enhancing the efficiency of diagnostic processes.

Online medical repositories provide a platform for users to share information and dynamically access abundant electronic health data. It is important to determine whether case report information can assist the general public in appropriately managing their diseases. Therefore, this paper aims to introduce a novel deep learning-based method that allows non-professionals to make inquiries using ordinary vocabulary, retrieving the most relevant case reports for accurate and effective health information.

The dataset of case reports was collected from both the patient-generated research network and the digital medical journal repository. To enhance the accuracy of obtaining relevant case reports, the authors propose a retrieval approach that combines BERT and BiLSTM methods. The authors identified representative health-related case reports and analyzed the retrieval performance, as well as user judgments.

This study aims to provide the necessary functionalities to deliver relevant health case reports based on input from ordinary terms. The proposed framework includes features for health management, user feedback acquisition and ranking by weights to obtain the most pertinent case reports.

This study contributes to health information systems by analyzing patients' experiences and treatments with the case report retrieval model. The results of this study can provide immense benefit to the general public who intend to find treatment decisions and experiences from relevant case reports.