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Abstract Background Health policy competencies of regional organizations include mandates to create regional health laws and policies, as well as authorities that allow member states to undertake collective actions in the health field. The examination of the health policy competencies of regional organizations is essential, as it constitutes an important prerequisite for regional organizations to govern regional health. This study aims to map the development trajectory of health policy competencies in regional organizations worldwide and investigate their potential correlates. This will contribute to the enhanced promotion of both existing and new regional health cooperation. Methods This retrospective analysis utilized the health policy competencies of the 76 regional organizations worldwide from 1945 to 2015, as investigated in the Regional Organizations Competencies Database. By aggregating member state data from various sources such as the IHME Global Burden of Disease 2019, the World Bank, and the World Trade Organization, we extracted the mean values and coefficients of variation for the covariates in regional organization characteristics, socioeconomic and demographic factors, health status and health-system capacity. The correlation between changes in the health policy scope of regional organizations and independent variables was analyzed using Poisson pseudo-likelihood regression with multiple levels of fixed effects. Results From 1945 to 2015, the number of regional organizations with health policy competencies experienced a slow growth stage before 1991 and an explosive growth stage post-1991. By 2015, 48 out of the 71 existing regional organizations had developed their health policy competencies, yet 26 (54.2%) of these organizations possessed only 1–2 health policy competencies. An enhancement in the health policy scope of a regional organization correlated with its founding year, a greater number of policy fields, higher under-five mortality, and larger disparities in trade and healthcare access and quality indexes among member states. In contrast, larger disparities in population, under-five mortality and health worker density among member states, along with more hospital beds per capita, were negatively correlated with the expansion of a regional organization’s health policy scope. Conclusion Since 1991, there has been a surge of interest in health among regional organizations, although health remains a secondary priority for them. The health policy competencies of regional organizations are pivotal for promoting social equity within regional communities. Its establishment is also closely linked to the level and disparities among member states in aspects such as trade, population, child mortality rates, and health system capacity.

Abstract Many real-world engineering problems need to balance different objectives and can be formatted as multi-objective optimization problem. An effective multi-objective algorithm can achieve a set of optimal solutions that can make a tradeoff between different objectives, which is valuable to further explore and design. In this paper, an improved multi-objective differential evolution algorithm (MOEA/D/DEM) based on a decomposition strategy is proposed to improve the performance of differential evolution algorithm for practical multi-objective nutrition decision problems. Firstly, considering the neighborhood characteristic, a neighbor intimacy factor is designed in the search process for enhancing the diversity of the population, then a new Gaussian mutation strategy with variable step size is proposed to reduce the probability of escaping local optimum area and improve the local search ability. Finally, the proposed algorithm is tested by classic test problems (DTLZ1-7 and WFG1-9) and applied to the multi-objective nutrition decision problems, compared to the other reported multi-objective algorithms, the proposed algorithm has a better search capability and obtained competitive results.

Abstract Background Ischemic heart disease causes a high disease burden globally and numerous challenges in treatment, particularly in developing countries such as China. The National Chest Pain Centers Program (NCPCP) was launched in China as the first nationwide, hospital-based, comprehensive, continuous quality improvement (QI) program to improve early diagnosis and standardized treatment of acute coronary syndromes (ACS) and improve patients’ clinical outcomes. With implementation and scaling up of the NCPCP, we investigated barriers and enablers in the NCPCP implementation process and provided examples and ideas for overcoming such barriers. Methods We conducted a nationally representative survey in six cities in China. A total of 165 key informant interviewees, including directors and coordinators of chest pain centers (CPCs) in 90 hospitals, participated in semi-structured interviews. The interviews were transcribed verbatim, translated into English, and analyzed in NVivo 12.0. We used the Consolidated Framework for Implementation Research (CFIR) to guide the codes and themes. Results Barriers to NCPCP implementation mainly arose from nine CFIR constructs. Barriers included the complexity of the intervention (complexity), low flexibility of requirements (adaptability), a lack of recognition of chest pain in patients with ACS (patient needs and resources), relatively low government support (external policies and incentives), staff mobility in the emergency department and other related departments (structural characteristics), resistance from related departments (networks and communications), overwhelming tasks for CPC coordinators (compatibility), lack of available resources for regular CPC operations (available resources), and fidelity to and sustainability of intervention implementation (executing). Enablers of intervention implementation were inner motivation for change (intervention sources), evidence strength and quality of intervention, relatively low cost (cost), individual knowledge and beliefs regarding the intervention, pressure from other hospitals (peer pressure), incentives and rewards of the intervention, and involvement of hospital leaders (leadership engagement, engaging). Conclusion Simplifying the intervention to adapt routine tasks for medical staff and optimizing operational mechanisms between the prehospital emergency system and in-hospital treatment system with government support, as well as enhancing emergency awareness among patients with chest pain are critically important to NCPCP implementation. Clarifying and addressing these barriers is key to designing a sustainable QI program for acute cardiovascular diseases in China and similar contexts across developing countries worldwide. Trial registration This study was registered in the Chinese Clinical Trial Registry ( ChiCTR 2100043319 ), registered 10 February 2021.

With the rapid development of the construction of high-pressure, large-diameter and long-distance X80 pipelines in China, Stress Corrosion Cracking (SCC) has become an important factor threatening the safe operation of pipelines. Herein, the key influencing factors of SCC of X80 pipeline steel in soil environment were summarized and analyzed. The influence of Alternating Current (AC) stray current on the SCC behavior of X80 pipeline steel and its influencing mechanism were analyzed, and it was pointed out that the alternating current and the elastic stress can cooperate with each other to jointly promote the anodic dissolution of X80 pipeline steel and destroy the state of dense corrosion product film on the steel surface, further accelerating the corrosion. In addition, the research status of the SCC mechanism of pipeline steel caused by Sulfate Reducing Bacteria (SRB) in soil environment was reviewed. It was concluded that the microbiologically-assisted cracking mechanism of X80 pipeline steel was mainly associated with the physiological activity of bacteria, its metabolites and the formation of biological film. Finally, suggestions on the selection of cathodic protection potential of X80 pipeline steel under SRB were proposed. Further, it is also recommended to include the microbial factors into the SCC evaluation system of pipeline steel. Therefore, the research results can provide theoretical guidance for the design and safety service of high-strength steel pipeline in China.

With the continuous enrichment of social network applications, such as TikTok, Weibo, Twitter, and others, social media have become an indispensable part of our lives. Web users can participate in their favorite events or pay attention to people they like. The “heterogeneous” influence between events and users can be effectively modeled, and users’ potential future behaviors can be predicted, so as to facilitate applications such as recommendations and online advertising. For example, a user’s favorite live streaming host (user) recommends certain products (event), can we predict whether the user will buy these products in the future? The majority of studies are based on a homogeneous graph neural network to model the influence between users. However, these studies ignore the impact of events on users in reality. For instance, when users purchase commodities through live streaming channels, in addition to the factors of the host, the commodity is also a key factor that influences the behavior of users. This study designs an influence prediction model based on a heterogeneous neural network HetInf. Specifically, we first constructed the heterogeneous social influence network according to the relationship between event nodes and user nodes, then sampled the user heterogeneous subgraph for each user, extracted the relevant node features, and finally predicted the probability of user behavior through the heterogeneous neural network model. We conducted comprehensive experiments on two large social network datasets. Furthermore, the experimental results show that HetInf is significantly superior to the previous homogeneous neural network methods.

With the rapid popularity of agent technology, a public opinion early warning agent has attracted wide attention. Furthermore, a deep learning model can make the agent more automatic and efficient. Therefore, for the agency of a public opinion early warning task, the deep learning model is very suitable for completing tasks such as popularity prediction or emergency outbreak. In this context, improving the ability to automatically analyze and predict the virality of information cascades is one of the tasks that deep learning model approaches address. However, most of the existing studies sought to address this task by analyzing cascade underlying network structure. Recent studies proposed cascade virality prediction for agnostic-networks (without network structure), but did not consider the fusion of more effective features. In this paper, we propose an innovative cascade virus prediction model named CasWarn. It can be quickly deployed in intelligent agents to effectively predict the virality of public opinion information for different industries. Inspired by the agnostic-network model, this model extracts the key features (independent of the underlying network structure) of an information cascade, including dissemination scale, emotional polarity ratio, and semantic evolution. We use two improved neural network frameworks to embed these features, and then apply the classification task to predict the cascade virality. We conduct comprehensive experiments on two large social network datasets. Furthermore, the experimental results prove that CasWarn can make timely and effective cascade virality predictions and verify that each feature model of CasWarn is beneficial to improve performance.

Objective This study was performed to investigate the analgesic effects of intramuscular injection of dezocine-based local anesthesia in patients undergoing inguinal hernia repair. Methods A total of 120 patients underwent local herniorrhaphy from July 2015 to December 2016 and were randomly divided into 2 groups: the dezocine group, in which a preoperative intramuscular injection of dezocine was administered, and the control group, in which no dezocine injection was administered. The patients’ pain and comfort levels were evaluated at 30 minutes, 2 hours, 6 hours, 12 hours, and 24 hours postoperatively. Results The visual analog scale scores at 30 minutes, 2 hours, 6 hours, 12 hours, and 24 hours were significantly lower in the dezocine than control group. In the Bruggemann comfort scale evaluation, patients who received dezocine injections showed significantly greater comfort than those in the control group at 30 minutes, 2 hours, 6 hours, 12 hours, and 24 hours. No adverse reactions occurred in the dezocine group. Conclusions Dezocine-based local anesthesia can attenuate postoperative pain and increase the comfort level during and after herniorrhaphy.

The recent development of the mobile Internet and the rise of social media have significantly enriched the way people access information. Accurate modeling of the probability of information propagation between users is essential for studying information dissemination issues in social networks. As the dissemination of information is inseparable from the interactions between users, the probability of propagation can be characterized by such interactions. In general, there are differences in the dissemination modes of information that carry different topics in a real social network. Using these factors, we propose a method (TMIVM) to measure the mutual influence between users at the topic level. The method associates two vectorization parameters for each user—an influence vector and a susceptibility vector—where the dimensions of the vector represent different topic categories. The magnitude of the mutual influence between users on different topics can be obtained by the product of the corresponding elements of the vectors. Specifically, in this article, we fit a social network historical information cascade data through Survival Analysis to learn the parameters of the influence and susceptibility vectors. The experimental results on a synthetic data set and a real Microblog data set show that this method better measures the propagation probability and information cascade predictions compared to other methods.

Large numbers of children around the world are learning tone languages, but few studies have examined the acoustic properties of children's early tone productions. Even more scarce are acquisition studies on tone sandhi, a tone change phenomenon which alters the surface realization of lexical tones. Two studies using perceptual coding report the emergence of lexical tone and tone sandhi at around 2 years (Li and Thompson, 1977; Hua and Dodd, 2000). However, the only acoustic study available shows that 3-year-olds are not yet adult-like in their lexical tone productions (Wong, 2012). This raises questions about when children's productions become acoustically adult-like and how their tone productions differ from those of adults. These questions were addressed in the current study which compared Mandarin-speaking pre-schoolers' (3–5-year-olds) tone productions to that of adults. A picture naming task was used with disyllabic real words familiar to pre-schoolers. Overall children produced appropriate tone contours for all tones, i.e., level for tone 1, rising for tones 2, 3 and full sandhi, falling for tone 4 and half sandhi. However, children's productions were not adult-like for tones 3, 4, and the sandhi forms, in terms of coordinating pitch range, slope and curvature, with little evidence of development across ages. These results suggest a protracted process in achieving adult-like acoustic realization of both lexical and sandhi tones.

Credit Card Fraud Detection (CCFD) is an essential technology for banking institutions to control fraud risks and safeguard their reputation. Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD, which significantly impact classification models' performance. To address these issues, this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks (MFGAN). The MFGAN model consists of two modules: a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space, and a balance module based on the generative adversarial network to decrease the class imbalance ratio. The effectiveness of theMFGAN model is validated on two actual credit card datasets. The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed, and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments. Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall, F1, and Area Under the Curve (AUC) metrics, which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses. [ABSTRACT FROM AUTHOR]