his study explores the relationship between carbon emissions and firm financial performance across industries and how green electricity adoption moderates this effect. Using 9,925 firm-year observations from the Taiwan Economic Journal, we estimate panel regressions and compare Fixed Effects robust and Driscoll-Kraay (1998) standard errors to ensure robustness. Two-way and three-way interaction models reveal a green electricity paradox: biotech firms benefit, while financial and high-tech sectors decline. These findings highlight the need for sector-specific ESG investment models and tailored policy support to ensure an equitable green transition. The study contributes by integrating environmental-financial dynamics with industry heterogeneity in emerging markets

Given the increasing intensity of climate change, members of the public perceive its occurrence and participate in pro-environmental behaviours (PEBs) that are expected to alleviate severe climate situations. Related scholarship has paid much attention to how determining factors affect public actions and proven the gaps between pro-environmental behavioural intentions (PEBIs) and PEBs, but little research has focused on strategies to bridge the intention-behaviour gap. Hence, this study explores the influence of climate change perception (CCP) on PEBs and the role of climate literacy (CL) in addressing the gap. We use a conditional process model and a sample of 1,668 residents of the mainland China. Our results reveal that public often have higher intentions to behave more pro-environmentally but rarely convert them into actual behaviours. CCP triggers positive intentions and behaviours towards the environment, and PEBIs partly and significantly mediate the majority effect of CCP on PEBs. The mediating role of individual intention in the process of other factors influencing public behaviour should not be ignored. CL promotes stronger PEBIs and further drives individuals to converse their intentions into behaviours, bridging the intention-behaviour gap. Additionally, we find that if the public fail to perceive climate change, CL alone is ineffective. These findings deepen our understanding of the internal and external factors that drive public participation, thereby providing policymakers with insights for bridging the gap from a literacy perspective.

INTRODUCTION:Optimal ovarian stimulation (OS) selection is critical for IVF success, but expert-based decisions often lack consistency in outcomes, cost-efficiency, and personalization, highlighting the need for more individualized and data-driven approaches. OBJECTIVES:This study propose an artificial intelligence (AI) system that analyzes extensive IVF-ET cycles to uncover OS-pregnancy outcome relationships, enabling personalized treatment recommendations while improving success rates and minimizing unnecessary costs. METHODS:This study analyzed anonymized data from 17,791 patients undergoing OS and IVF/ICSI at Tongji Hospital between May 2015 and May 2019. An adaptive AI model was developed to predict key indicators-including progesterone (P), number of oocytes retrieved (NOR), estradiol (E2), and endometrial thickness (EMT) on the hCG day-by integrating personal characteristics, ovarian reserve, and etiological factors. This model facilitated personalized OS selection, pregnancy outcome grading, and the development of an AI-driven clinical decision support system (CDSS). RESULTS:The key indicators-progesterone (P), number of oocytes retrieved (NOR), estradiol (E2), and endometrial thickness (EMT) on the hCG day-were used to establish a pregnancy grading system. Pregnancy rates are stratified as follows: Level IV (Total Score 15-16), 0.55; Level III (Total Score 13-14), 0.44; Level II (Total Score 11-12), 0.24; and Level I (Total Score 4-10), 0.07. After OS optimization, 1,355 patients who were initially at level I were elevated to a better level. Of the 2,341 patients initially in level II, 2,290 improved, and of the 3,839 initially in level III, 1,448 improved. Patients elevated to level IV accounted for 80 percent of all cases. The CDSS prioritized a GnRH antagonist regimen for 54.64 % of patients, resulting in per-patient time savings of 15.39-33.48 days and cost reductions of ¥989-¥2,623 compared to non-optimal to antagonist. Scaled to China's > 1 million ART cycles annually, this corresponds to projected direct savings of approximately ¥0.54-1.43 billion per year. In the new evaluation datasets (n = 4,251), implementation of CDSS recommendations increased the clinical pregnancy rate from 0.452 to 0.512 (p < 0.001) and reduced mean per-cycle cost from ¥7,385 to ¥7,242 (p = 0.018), demonstarting cost-effectiveness dominance with ICER saving of ¥2,383 per additional clinical pregnancy. CONCLUSION:This AI-assisted CDSS streamlines clinicians' decision-making by enabling efficient and accurate initial judgments on OS, standardizing and personalizing recommendations, and optimizing OS for effectiveness and cost-efficiency.

This paper presents a supply chain (SC) optimization model that balances economic, environmental, and social objectives by aiming to maximize profits while minimizing greenhouse gas emissions and service level inequalities. It simulates real-world SC issues using a fourechelon facility model with variable demands from three markets. We utilize multi-objective Markov decision processes (MOMDP) through multiobjective reinforcement learning with decomposition (MORL/D), paired with weighted sum proximal policy optimization (PPO), and compare them using a non-dominated sorting genetic algorithm II (NSGA-II). The decision variables are production and delivery quantities, leading to Pareto front sets that illustrate optimal trade-offs. Key contributions include defining a three-objective SC under a MOMDP framework, introducing a Python-based SC simulation tool called Messiah, pioneering MORL/D in multi-objective SC optimization, and comparing it with PPO and NSGA-II. Our findings reveal that MORL/D achieves more balanced outcomes in optimality, diversity, and density, with enhanced hypervolume and expected utility metrics through knowledge sharing.

Climate change has markedly increased adverse effects on human health and economic growth1-3. However, few studies have differentiated the impacts of extreme temperatures at the city level and analysed the future implications for human health under various climate change scenarios4-6. Here we leverage data on historical relationships among six kinds of climate-sensitive diseases (CSDs) and associated hospitalizations and temperatures across 301 cities (more than 90% of all cities) and more than 7,000 hospitals in China, and use a nonlinear distributed lag model. This study projects hospitalization risks associated with extreme temperatures through to the year 2100 and develops the hospitalization burden economic index to assess the burden under three carbon emission scenarios across cities. Five dimensions, including spatial distribution, disease categories, population age groups, future time horizons and carbon emission development pathways, have been evaluated. Historical data indicate a higher incidence of temperature-related risks among the CSDs in northwestern and southwestern China. Notably, gestation-related disease risks are associated with increased vulnerability to extreme heat in specific regions. The projections show that under current thermal conditions without adaptations, the excess hospitalizations from extreme heat will reach 0.6, 3.8 and 5.1 million by 2100 under the low-, middle- and high-emission scenarios, respectively. These findings highlight the need for targeted mitigation strategies to reduce uneven climate-related hospitalization risks and economic burdens while accounting for differences in city geography, extreme temperatures, population groups and carbon emission development pathways.

Currently, individual artificial intelligence (AI) algorithms face significant challenges in effectively diagnosing and predicting early stage emerging serious diseases. Our investigation indicates that these challenges primarily arise from insufficient clinical treatment data, leading to inadequate model training and substantial disparities among algorithm outcomes. Therefore, this study introduces an adaptive framework aimed at increasing prediction accuracy and mitigating instability by integrating various AI algorithms. In analyzing two cohorts of early cases of the coronavirus disease 2019 (COVID-19) in Wuhan, China, we demonstrate the reliability and precision of the adaptive combined learning algorithm. Employing an adaptive combination with three feature importance methods (Random Forest (RF), Scalable end-to-end Tree Boosting System (XGBoost), and Sparsity Oriented Importance Learning (SOIL)) for two cohorts, we identified 23 clinical features with significant impacts on COVID-19 outcomes. Subsequently, the adaptive combined prediction leveraged and enhanced the advantages of individual methods based on three forecasting algorithms (RF, XGBoost, and Logistic regression). The average accuracy for both cohorts exceeded 0.95, with the area under the receiver operating characteristics curve (AUC) values of 0.983 and 0.988, respectively. We established a severity grading system for COVID-19 based on the combined probability of death. Compared to the original classification, there was a significant decrease in the number of patients in the severe and critical levels, while the levels of mild and moderate showed a substantial increase. This severity grading system provides a more rational grading in clinical treatment. Clinicians can utilize this system for effective and reliable preliminary assessments and examinations of patients with emerging diseases, enabling timely and targeted treatment.

Accumulating evidence has revealed that chronic unresolved inflammation can cause significant tissue damage and can be a key mediator of advanced heart failure (HF). Resolvin (Rv) D2, a member of specialized pro-resolving lipid mediators (SPMs), plays a protective role in various diseases by facilitating resolution. However, whether RvD2 participates in the pathogenesis of HF is still unclear. Our study demonstrated that RvD2 treatment mitigated cardiac remodeling and improved cardiac function in HF mice induced by pressure overload. The absence of G protein-coupled receptor 18 (GPR18), an endogenous receptor for RvD2, abolished the beneficial effects of RvD2 on HF. Additionally, RvD2 inhibited inflammatory responses and Ly6Chigh macrophage polarization during both early and late inflammatory stages involved in HF. Further investigation revealed that bone marrow transplantation from Gpr18 deficient mice into WT mice blocked the protective effects of RvD2 in HF mice. Moreover, Gpr18 deficiency impeded RvD2's capacity to downregulate inflammatory responses and Ly6Chigh macrophage polarization. Consistent with experiments in vivo, RvD2 treatment in bone marrow-derived macrophages (BMDMs) reduced inflammatory responses through its receptor GPR18. Mechanistically, RvD2 suppressed the phosphorylation of STAT1 and NF-κB p65, and the effects of RvD2 were reversed by the application of STAT1 or NF-κB p65 agonists in BMDMs. In conclusion, RvD2/GPR18 axis improved cardiac remodeling and function in pressure overload-induced HF mice by modulating macrophage phenotype via STAT1 and NF-κB p65 pathways. Our findings underscore the anti-inflammatory potential of RvD2/GPR18 axis, suggesting that RvD2/GPR18 axis may be a potential strategy for the treatment of HF.

As a basic tool for understanding and making informed decisions about global warming, climate literacy could potentially affect the whole process from individual awareness to public engagement with global climate change. We conducted a nationwide online survey (N = 3067) to assess climate literacy in China and investigate its role in climate change concern and climate policy support. Respondents in our sample were generally well informed about the cause and public engagement dimensions of climate literacy, while demonstrated polarized performance in regard to the consequences of climate change. Climate literacy is a stronger predictor of climate change concern and policy support than other variables such as demographics, experience, and values and can largely enhance the effects of media coverage through the mediation effect. Education and media coverage are found to be significantly associated with climate literacy, while climate experience has little to no effect on climate literacy. Our results somewhat undermine the central role of climate change concern in climate communications and public engagement. Instead, enhancing public climate literacy by disseminating scientific and result-based information from reliable institutions seems to be a more promising path in China.

Environmental pollution has become a hot topic of concern for the government, academia and the public. The evaluation of environmental health should not only relate to environmental quality and exposure channels but also the level of economic development, social environmental protection responsibility and public awareness. We put forward the concept of the "healthy environment" and introduced 27 environmental indicators to evaluate and classify the healthy environment of 31 provinces and cities in China. Seven common factors were extracted and divided into economic, medical, ecological and humanistic environment factors. Based on the four environmental factors, we classify the healthy environment into five categories-economic leading healthy environment, robust healthy environment, developmental healthy environment, economic and medical disadvantageous healthy environment and completely disadvantageous healthy environment. The population health differences among the five healthy environment categories show that economic environment plays a major role in population health. Public health in regions with sound economic environment is significantly better than that in other areas. Our classification result of healthy environment can provide scientific support for optimizing environmental countermeasures and realizing environmental protection.

为进一步优化国家自然科学基金优秀青年科学基金项 目(港澳)的组织实施过程,基于2019～2020年度该项 目申报基本情况,对自2019年度项目实施以来的科研人员参与情况、经费管理和基金建议进行调查和分析,了解港澳科研人员的真实诉求.通过对港澳8家依托单位275位科研人员开展问卷调查,研究发现:该项目在港澳科研人员中获得了较高的支持度和认同感,科研人员对项 目的参与意愿较高,对基金委在项目组织实施过程中的工作和贡献比较满意.从调查结果上,大部分科研人员建议能够通过优化基金申请流程、完善专家评审标准等进一步优化项目申报.

The factors influencing residents health have become complex and intertwined with the development of economy and society. Traditional research with a single factor on health will not provide an accurate picture of the situation. This paper collects data on economic, environmental and social factors to estimate their impact on regional health. Considering the data is multi-source and complex, this paper proposes a combined feature importance algorithm, which weighted the feature importance of RF, XGB and SOIL. The algorithm does not depend on the data and adaptively approximates the true results. The results show that economic factors have a significant and direct impact on health, environmental factors have a lag correlation with health level, and social factors have a more complicated effect on health. Finally, we provide policy suggestions for health on economic, environmental, and social development.

AbstractTransient receptor potential ankyrin 1 (TRPA1) plays an important role in different cardiovascular diseases. However, the role of TRPA1 in dilated cardiomyopathy (DCM) remains unclear. Here, we aimed to investigate the role of TRPA1 in DCM induced by doxorubicin (DOX) and explore its possible mechanisms. GEO data were used to explore the expression of TRPA1 in DCM patients. DOX (2.5 mg/kg/week, 6 weeks, i.p.) was used to induce DCM. Bone marrow‐derived macrophages (BMDMs) and neonatal rat cardiomyocytes (NRCMs) were isolated to explore the role of TRPA1 in macrophage polarization, cardiomyocyte apoptosis, and pyroptosis. In addition, DCM rats were treated with the TRPA1 activator, cinnamaldehyde to explore the possibility of clinical translation. TRPA1 expression was increased in left ventricular (LV) tissue in DCM patients and rats. TRPA1 deficiency aggravated the cardiac dysfunction, cardiac injury, and LV remodeling in DCM rats. In addition, TRPA1 deficiency promoted the M1 macrophage polarization, oxidative stress, cardiac apoptosis, and pyroptosis induced by DOX. RNA‐seq results showed that TRPA1 knockout promoted the expression of S100A8, an inflammatory molecule that belongs to the family of Ca2+‐binding S100 proteins, in DCM rats. Furthermore, S100A8 inhibition attenuated M1 macrophage polarization in BMDMs isolated from TRPA1 deficiency rats. Recombinant S100A8 promoted the apoptosis, pyroptosis, and oxidative stress in primary cardiomyocytes stimulated with DOX. Finally, TRPA1 activation via cinnamaldehyde alleviated the cardiac dysfunction and reduced S100A8 expression in DCM rats. Taken together, these results suggested that TRPA1 deficiency aggravates DCM by promoting S100A8 expression to induce M1 macrophage polarization and cardiac apoptosis.

The Healthy China Strategy puts realistic demands for residents' health levels, but the reality is that various factors can affect health. In order to clarify which factors have a great impact on residents' health, based on China's provincial panel data from 2011 to 2018, this paper selects 17 characteristic variables from the three levels of economy, environment, and society and uses the XG boost algorithm and Random forest algorithm based on recursive feature elimination to determine the influencing variables. The results show that at the economic level, the number of industrial enterprises above designated size, industrial added value, population density, and per capita GDP have a greater impact on the health of residents. At the environmental level, coal consumption, energy consumption, total wastewater discharge, and solid waste discharge have a greater impact on the health level of residents. Therefore, the Chinese government should formulate targeted measures at both economic and environmental levels, which is of great significance to realizing the Healthy China strategy.

Environmental pollution damages public health and affects economic development. Environmental regulation is the main way for the government to solve environmental pollution. So what type of environmental regulation works better for public health and economic development? Can environmental regulation have an influence on economic development through public health? To solve these problems, this research uses China’s provincial panel data from 2013 to 2017 to divide environmental regulation into command-control policy tools and economic incentive policy tools and uses the mediating effect model to examine the relationship among environmental regulation, public health and economic development. The results show that: (1) There is a positive correlation between economic incentive policy tools and economic development; while no significant relationship between command-control policy tools and economic development is founded; (2) The relationship between command-control policy tools and public health is not significant, while the relationship between economic incentive policy tools and public health is positive; (3) Public health does not play a mediating role between command-control policy tools and economic development but plays a partial mediating role between economic incentive policy tools and economic development. Therefore, the government should strengthen the use of economic incentive policy tools to promote public health and sustainable economic development.

This paper analyses the interaction between the novel coronavirus pandemic (COVID-19), unemployment rate, stock market, consumer confidence index (CCI), and economic policy uncertainty (EPU) index in China within a time-frequency framework. We compare the changes in economic indicators during the global financial crisis (GFC) and study the different impacts of the two events on China's economy. An unprecedented impact of COVID-19 shocks on the unemployment rate, CCI, EPU index, and stock market volatility over the low frequency bands is uncovered by applying the coherence wavelet method to China monthly data. The COVID-19 effect on the stock market volatility and the EPU index is substantially higher than on the unemployment rate and the CCI. On the contrary, the GFC's impact on the unemployment rate is much greater than that on the EPU index and CCI. Additionally, the impact of the GFC on the economy is more cyclical in the long-term, while the COVID-19 pandemic is a short-term shock with a relatively short oscillation cycle. This study concludes that the economic impact of COVID-19 will not spread into a financial crisis for China and believe that the COVID-19 pandemic is more of a health event than an economic crisis for Chinese economy.

The application of artificial intelligence (AI) methods in medical field is increasing year by year; however, few studies have applied AI methods in the reproductive field. In view of the complexity of infertility diagnosis and treatment, a machine learning‐based risk scoring system for infertility was constructed in this paper to help clinicians better grasp the patient's condition. First, eight key features of infertility are screened out by feature selection. Second, the entropy‐based feature discretization method was used to divide the feature abnormal intervals, and the random forest was used to determine the weight of each feature. Finally, the pregnancy outcome can be predicted according to the overall risk score of patients, which is helpful for doctors to choose targeted treatment more efficiently. It is worth noting that, to further improve the accuracy of the diagnosis, we also divided the patients into age groups and constructed the corresponding risk scoring system for patients of different age groups. The stability test results show the good performance of the system. The risk scoring system for infertility built in this paper is a meaningful exploration of the application of AI in the field of reproduction.

Different endometrial patterns have an important effect on the relationship between endometrial thickness (EMT) and clinical pregnancy rate. There is a significant difference in age, selection of cycle protocols, and clinical pregnancy rates among four groups with diverse endometrial patterns. This retrospective study aimed to assess the association between EMT on human chorionic gonadotropin (HCG) administration day and the clinical outcome of fresh in vitro fertilization (IVF). The 5th, 50th, and 95th percentiles for EMT were determined as 8, 11, and 14 mm, respectively. Patients were sub-divided into four groups based on their EMT in different endometrial patterns (Group 1: < 8 mm; Group 2: ≥ 8 and ≤ 11 mm; Group 3: > 11 and ≤ 14 mm; Group 4: > 14 mm). We divided patients into three groups based on their endometrial pattern and evaluated the correlation between EMT and clinical pregnancy rate. We found a positive correlation between pregnancy rates and EMT in all endometrial patterns. Multiple logistic regression analysis proved age, duration of infertility, cycle protocols, number of embryos transferred, progesterone on HCG day, endometrial patterns, and EMT have significant effects on clinical pregnancy rates. Meanwhile, there was a significant difference in age, selection of cycle protocols, and clinical pregnancy rates among four groups with diverse endometrial patterns. Different endometrial patterns have an important effect on the relationship between EMT and clinical pregnancy rate.

Cardiac dysfunction is a well-recognized complication of sepsis and is associated with the outcome and prognosis of septic patients. Evidence suggests that Il12a participates in the regulation of various cardiovascular diseases, including heart failure, hypertension and acute myocardial infarction. However, the effects of Il12a in sepsis-induced cardiac dysfunction remain unknown. In our study, lipopolysaccharide (LPS) and cecal ligation and puncture (CLP) model were used to mimic sepsis, and cardiac Il12a expression was assessed. In addition, Il12a knockout mice were used to detect the role of Il12a in sepsis-related cardiac dysfunction. We observed for the first time that Il12a expression is upregulated in mice after LPS treatment and macrophages were the main sources of Il12a. In addition, our findings demonstrated that Il12a deletion aggravates LPS-induced cardiac dysfunction and injury, as evidenced by the increased serum and cardiac levels of lactate dehydrogenase (LDH) and cardiac creatine kinase-myocardial band (CK-MB). Moreover, Il12a deletion enhances LPS-induced macrophage accumulation and drives macrophages toward the M1 phenotype in LPS-treated mice. Il12a deletion also downregulated the activity of AMP-activated protein kinase (AMPK) but increased the phosphorylation levels of p65 (p-p65) and NF-κB inhibitor alpha (p-IκBα). In addition, Il12a deletion aggravates CLP-induced cardiac dysfunction and injury. Treatment with the AMPK activator AICAR abolishes the deterioration effect of Il12a deletion on LPS-induced cardiac dysfunction. In conclusion, Il12a deletion aggravated LPS-induced cardiac dysfunction and injury by exacerbating the imbalance of M1 and M2 macrophages. Our data provide evidence that Il12a may represent an attractive target for sepsis-induced cardiac dysfunction.