青蒿素是一种从菊科蒿属植物黄花蒿(Artemisia annua)中分离出来的倍半萜类天然产物,青蒿琥酯(artesunate)是其衍生物之一,具有经济、速效、低毒、不易产生耐受等特点。目前临床主要用于脑型疟疾及各种重症疟疾的抢救,对疟原虫红内期有...青蒿素是一种从菊科蒿属植物黄花蒿(Artemisia annua)中分离出来的倍半萜类天然产物,青蒿琥酯(artesunate)是其衍生物之一,具有经济、速效、低毒、不易产生耐受等特点。目前临床主要用于脑型疟疾及各种重症疟疾的抢救,对疟原虫红内期有强大且快速杀灭的作用,能够迅速控制临床发作及症状。除此之外,青蒿琥酯还具有抗肿瘤、抗病毒、抗肝纤维化、抗炎、抗菌、保护肝细胞、免疫调节等药理作用,抑制细胞增殖、诱导细胞凋亡及下调血症水平。以青蒿素类化合物为基础的联合疗法(ACTs)是多国治疗疟疾的一线方法,主要包括蒿甲醚-本芴醇、青蒿琥酯-阿莫地喹和青蒿甲醚-苯甲酰胺等。近年来国内外学者对青蒿琥酯进行了研究,发现相对于单一疗法而言,青蒿琥酯联合疗法在增强药理作用、缩短用药时长和减少不良反应等方面更具有优越性。该文通过Web of Science核心合集(Web of Science Core Collection)系统检索并通过CiteSpace(6.2.1)软件对其进行整合,综述了近5年来青蒿琥酯联合其他药物在抗疟、抗肿瘤、抗菌和抗病毒等方面的作用机制并对其进行阐述,旨在为进一步合理开发利用青蒿琥酯及新药研发提供一定的理论依据。展开更多
In the era of Industry 4.0,conditionmonitoring has emerged as an effective solution for process industries to optimize their operational efficiency.Condition monitoring helps minimize unplanned downtime,extending equi...In the era of Industry 4.0,conditionmonitoring has emerged as an effective solution for process industries to optimize their operational efficiency.Condition monitoring helps minimize unplanned downtime,extending equipment lifespan,reducing maintenance costs,and improving production quality and safety.This research focuses on utilizing Bayesian search-based machine learning and deep learning approaches for the condition monitoring of industrial equipment.The study aims to enhance predictive maintenance for industrial equipment by forecasting vibration values based on domain-specific feature engineering.Early prediction of vibration enables proactive interventions to minimize downtime and extend the lifespan of critical assets.A data set of load information and vibration values from a heavy-duty industrial slip ring induction motor(4600 kW)and gearbox equipped with vibration sensors is used as a case study.The study implements and compares six machine learning models with the proposed Bayesian-optimized stacked Long Short-Term Memory(LSTM)model.The hyperparameters used in the implementation of models are selected based on the Bayesian optimization technique.Comparative analysis reveals that the proposed Bayesian optimized stacked LSTM outperforms other models,showcasing its capability to learn temporal features as well as long-term dependencies in time series information.The implemented machine learning models:Linear Regression(LR),RandomForest(RF),Gradient Boosting Regressor(GBR),ExtremeGradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Support Vector Regressor(SVR)displayed a mean squared error of 0.9515,0.4654,0.1849,0.0295,0.2127 and 0.0273,respectively.The proposed model predicts the future vibration characteristics with a mean squared error of 0.0019 on the dataset containing motor load information and vibration characteristics.The results demonstrate that the proposed model outperforms other models in terms of other evaluation metrics with a mean absolute error of 0.0263 and 0.882 as a coefficient of determination.Current research not only contributes to the comparative performance of machine learning models in condition monitoring but also showcases the practical implications of employing these techniques.By transitioning fromreactive to proactive maintenance strategies,industries canminimize downtime,reduce costs,and prolong the lifespan of crucial assets.This study demonstrates the practical advantages of transitioning from reactive to proactive maintenance strategies using ML-based condition monitoring.展开更多
AI-driven fintech industries face critical vulnerabilities from volatile rare earth and metallic mineral prices,geopolitical instability,and inflationary pressures.Sovereign inflation-linked bonds serve as incentives ...AI-driven fintech industries face critical vulnerabilities from volatile rare earth and metallic mineral prices,geopolitical instability,and inflationary pressures.Sovereign inflation-linked bonds serve as incentives for investors in technological industries,despite the risks associated with rising costs of goods.By analyzing global data(8 September 2020–9 September 2023)via cross-quantilogram,recursive cross-quantilogram and quantile vector autoregressive approaches,this study reveals how Russia–Ukraine geopolitical risk,sovereign inflation–linked bonds,rare earth and metallic mineral prices disrupt AI-driven fintech outputs.Key findings indicate that rising rare earth prices suppress fintech productivity in long-term growth periods,whereas sovereign inflation-linked bonds mitigate short-term inflationary risk.Geopolitical turmoil disproportionately harms fintech outputs during market downturns,with both mineral price volatility and conflict-driven shocks amplifying systemic instability in fintech outputs and sovereign inflation-linked bonds.These results urge policymakers to secure critical mineral supply chains,promote inflation-hedging financial instruments,and foster international cooperation to buffer AI-driven fintech sectors against geopolitical and resource-driven disruptions.展开更多
In Bahawalpur,a cross-sectional study assessed depression prevalence across age and gender groups,involving 442 participants from diverse socioeconomic backgrounds and settings.Utilizing the PHQ9 questionnaire,84%were...In Bahawalpur,a cross-sectional study assessed depression prevalence across age and gender groups,involving 442 participants from diverse socioeconomic backgrounds and settings.Utilizing the PHQ9 questionnaire,84%were found to meet depression levels.The findings revealed a higher incidence in females(88%)than males(79%),with the greatest disparity among young adults,particularly young women,due to factors like academic pressure and financial stress.School children had the lowest depression rates(68%),possibly due to better immunity.Elderly individuals exhibited more severe depression,likely related to aging and domestic challenges.The study’s findings highlight a significant variation in depression severity across different demographic groups,with an overall higher incidence in women.The research underscores the necessity for targeted mental health resources and interventions tailored to the specific needs of each demographic group.It also points to the importance of addressing academic and socioeconomic stressors to mitigate depression,particularly among young women.While the study provides valuable insights,it relies on self-reported data,which may introduce bias.Therefore,future research should include clinical assessments to validate these findings and ensure a more accurate representation of depression within the community.展开更多
This study’s main purpose is to use Bayesian structural time-series models to investigate the causal effect of an earthquake on the Borsa Istanbul Stock Index.The results reveal a significant negative impact on stock...This study’s main purpose is to use Bayesian structural time-series models to investigate the causal effect of an earthquake on the Borsa Istanbul Stock Index.The results reveal a significant negative impact on stock market value during the post-treatment period.The results indicate rapid divergence from counterfactual predictions,and the actual stock index is lower than would have been expected in the absence of an earthquake.The curve of the actual stock value and the counterfactual prediction after the earthquake suggest a reconvening pattern in the stock market when the stock market resumes its activities.The cumulative impact effect shows a negative effect in relative terms,as evidenced by the decrease in the BIST-100 index of -30%.These results have significant implications for investors and policymakers,emphasizing the need to prepare for natural disasters to minimize their adverse effects on stock market valuations.展开更多
In recent years, SLA and L2 learning motivation have received extensive attention of researchers and teachers across the globe but the issue remains underdeveloped and there are only some small-scale studies on this s...In recent years, SLA and L2 learning motivation have received extensive attention of researchers and teachers across the globe but the issue remains underdeveloped and there are only some small-scale studies on this subject in Pakistan. Among different factors that affect L2 learning motivation, the current study focuses on exploring differences in L2 learning motivation by college type （private vs. public） and major subject of study （Arts vs. Sciences）. Analyzing the questionnaire data from 547 first year college students, the study singles out different situation-specific factors that account for variation in ESL learning motivation. Results indicate that private college students have a higher motivation level and better achievements in ESL learning as compared to public college students. Public college students have strong instrumental motivation while private college students show preferences for an ideal L2 self. L2 motivation does not differ a great deal between students with different subjects of study but there is a big gap in the achievement of both groups. Arts majors＇ motivation depends heavily on their attitude towards English while science majors are instrumentally motivated to learn English. We also discuss some possible reasons for the differences in motivation and implications of the study for ESL teachers and learners.展开更多
Microplastics provide a new ecological niche for microorganisms,and the accumulation levels of microplastics(MPs)in terrestrial ecosystems are higher than those in marine ecosystems.Here,we applied the zymography to i...Microplastics provide a new ecological niche for microorganisms,and the accumulation levels of microplastics(MPs)in terrestrial ecosystems are higher than those in marine ecosystems.Here,we applied the zymography to investigate how MPs–polyethylene[PE],and polyvinyl chloride[PVC])at two levels(0.01%and 1%soil weight)impacted the spatial distribution of soil hydrolases,nutrient availability,and rice growth in paddy soil.MPs increased the above-ground biomass by 13.0%–15.5%and decreased the below-ground biomass by 8.0%–15.1%.Addition of 0.01%and 1%MPs reduced soil NH4+content by 18.3%–63.2%and 52.2%–80.2%,respectively.The average activities of N-and P-hydrolases increased by 0.8%–4.8%and 1.9%–6.3%with addition of MPs,respectively.The nutrient uptake by rice plants and the enzyme activities in hotspots increased with MP content in soil.The accumulation of MPs in paddy soil could provide an ecological niche that facilitates microbial survival,alters the spatial distribution of soil hydrolases,and decreases nutrient availability.展开更多
Aggregate demand or supply at equilibrium is commonly used as a representative of the macroeconomic activity of an economy whereby aggregate demand denotes the behaviour of individuals and households.However,aggregate...Aggregate demand or supply at equilibrium is commonly used as a representative of the macroeconomic activity of an economy whereby aggregate demand denotes the behaviour of individuals and households.However,aggregate demand can also directly affect environmental deterioration via changes in aggregate production.This study tried to explore this relationship,known as the demand-based Environmental Kuznets Curve(Demand EKC)and the role of different knowledge economy indicators.Knowledge economy indicators are proposed to influence consumption patterns,altering the demand EKC that empirical studies have understudied.For this purpose,secondary data for 147 countries were collected from 2008 to 2018,also classified as development-wise.This study found that aggregate demand significantly affects carbon emissions.The long-run results are estimated using the Fully Modified Ordinary Least Square method.Controlling factors like renewable energy consumption,population density,and financial development significantly affect carbon emissions in sample countries.This study has incorporated four pillars of a knowledge-based economy and the results showed that these indicators helped reduce consumption-related CO_(2) emissions.展开更多
文摘青蒿素是一种从菊科蒿属植物黄花蒿(Artemisia annua)中分离出来的倍半萜类天然产物,青蒿琥酯(artesunate)是其衍生物之一,具有经济、速效、低毒、不易产生耐受等特点。目前临床主要用于脑型疟疾及各种重症疟疾的抢救,对疟原虫红内期有强大且快速杀灭的作用,能够迅速控制临床发作及症状。除此之外,青蒿琥酯还具有抗肿瘤、抗病毒、抗肝纤维化、抗炎、抗菌、保护肝细胞、免疫调节等药理作用,抑制细胞增殖、诱导细胞凋亡及下调血症水平。以青蒿素类化合物为基础的联合疗法(ACTs)是多国治疗疟疾的一线方法,主要包括蒿甲醚-本芴醇、青蒿琥酯-阿莫地喹和青蒿甲醚-苯甲酰胺等。近年来国内外学者对青蒿琥酯进行了研究,发现相对于单一疗法而言,青蒿琥酯联合疗法在增强药理作用、缩短用药时长和减少不良反应等方面更具有优越性。该文通过Web of Science核心合集(Web of Science Core Collection)系统检索并通过CiteSpace(6.2.1)软件对其进行整合,综述了近5年来青蒿琥酯联合其他药物在抗疟、抗肿瘤、抗菌和抗病毒等方面的作用机制并对其进行阐述,旨在为进一步合理开发利用青蒿琥酯及新药研发提供一定的理论依据。
文摘In the era of Industry 4.0,conditionmonitoring has emerged as an effective solution for process industries to optimize their operational efficiency.Condition monitoring helps minimize unplanned downtime,extending equipment lifespan,reducing maintenance costs,and improving production quality and safety.This research focuses on utilizing Bayesian search-based machine learning and deep learning approaches for the condition monitoring of industrial equipment.The study aims to enhance predictive maintenance for industrial equipment by forecasting vibration values based on domain-specific feature engineering.Early prediction of vibration enables proactive interventions to minimize downtime and extend the lifespan of critical assets.A data set of load information and vibration values from a heavy-duty industrial slip ring induction motor(4600 kW)and gearbox equipped with vibration sensors is used as a case study.The study implements and compares six machine learning models with the proposed Bayesian-optimized stacked Long Short-Term Memory(LSTM)model.The hyperparameters used in the implementation of models are selected based on the Bayesian optimization technique.Comparative analysis reveals that the proposed Bayesian optimized stacked LSTM outperforms other models,showcasing its capability to learn temporal features as well as long-term dependencies in time series information.The implemented machine learning models:Linear Regression(LR),RandomForest(RF),Gradient Boosting Regressor(GBR),ExtremeGradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Support Vector Regressor(SVR)displayed a mean squared error of 0.9515,0.4654,0.1849,0.0295,0.2127 and 0.0273,respectively.The proposed model predicts the future vibration characteristics with a mean squared error of 0.0019 on the dataset containing motor load information and vibration characteristics.The results demonstrate that the proposed model outperforms other models in terms of other evaluation metrics with a mean absolute error of 0.0263 and 0.882 as a coefficient of determination.Current research not only contributes to the comparative performance of machine learning models in condition monitoring but also showcases the practical implications of employing these techniques.By transitioning fromreactive to proactive maintenance strategies,industries canminimize downtime,reduce costs,and prolong the lifespan of crucial assets.This study demonstrates the practical advantages of transitioning from reactive to proactive maintenance strategies using ML-based condition monitoring.
基金supported by the grant of the Russian Science Foundation(RSF Code:23-18-01065).
文摘AI-driven fintech industries face critical vulnerabilities from volatile rare earth and metallic mineral prices,geopolitical instability,and inflationary pressures.Sovereign inflation-linked bonds serve as incentives for investors in technological industries,despite the risks associated with rising costs of goods.By analyzing global data(8 September 2020–9 September 2023)via cross-quantilogram,recursive cross-quantilogram and quantile vector autoregressive approaches,this study reveals how Russia–Ukraine geopolitical risk,sovereign inflation–linked bonds,rare earth and metallic mineral prices disrupt AI-driven fintech outputs.Key findings indicate that rising rare earth prices suppress fintech productivity in long-term growth periods,whereas sovereign inflation-linked bonds mitigate short-term inflationary risk.Geopolitical turmoil disproportionately harms fintech outputs during market downturns,with both mineral price volatility and conflict-driven shocks amplifying systemic instability in fintech outputs and sovereign inflation-linked bonds.These results urge policymakers to secure critical mineral supply chains,promote inflation-hedging financial instruments,and foster international cooperation to buffer AI-driven fintech sectors against geopolitical and resource-driven disruptions.
文摘In Bahawalpur,a cross-sectional study assessed depression prevalence across age and gender groups,involving 442 participants from diverse socioeconomic backgrounds and settings.Utilizing the PHQ9 questionnaire,84%were found to meet depression levels.The findings revealed a higher incidence in females(88%)than males(79%),with the greatest disparity among young adults,particularly young women,due to factors like academic pressure and financial stress.School children had the lowest depression rates(68%),possibly due to better immunity.Elderly individuals exhibited more severe depression,likely related to aging and domestic challenges.The study’s findings highlight a significant variation in depression severity across different demographic groups,with an overall higher incidence in women.The research underscores the necessity for targeted mental health resources and interventions tailored to the specific needs of each demographic group.It also points to the importance of addressing academic and socioeconomic stressors to mitigate depression,particularly among young women.While the study provides valuable insights,it relies on self-reported data,which may introduce bias.Therefore,future research should include clinical assessments to validate these findings and ensure a more accurate representation of depression within the community.
文摘This study’s main purpose is to use Bayesian structural time-series models to investigate the causal effect of an earthquake on the Borsa Istanbul Stock Index.The results reveal a significant negative impact on stock market value during the post-treatment period.The results indicate rapid divergence from counterfactual predictions,and the actual stock index is lower than would have been expected in the absence of an earthquake.The curve of the actual stock value and the counterfactual prediction after the earthquake suggest a reconvening pattern in the stock market when the stock market resumes its activities.The cumulative impact effect shows a negative effect in relative terms,as evidenced by the decrease in the BIST-100 index of -30%.These results have significant implications for investors and policymakers,emphasizing the need to prepare for natural disasters to minimize their adverse effects on stock market valuations.
文摘In recent years, SLA and L2 learning motivation have received extensive attention of researchers and teachers across the globe but the issue remains underdeveloped and there are only some small-scale studies on this subject in Pakistan. Among different factors that affect L2 learning motivation, the current study focuses on exploring differences in L2 learning motivation by college type （private vs. public） and major subject of study （Arts vs. Sciences）. Analyzing the questionnaire data from 547 first year college students, the study singles out different situation-specific factors that account for variation in ESL learning motivation. Results indicate that private college students have a higher motivation level and better achievements in ESL learning as compared to public college students. Public college students have strong instrumental motivation while private college students show preferences for an ideal L2 self. L2 motivation does not differ a great deal between students with different subjects of study but there is a big gap in the achievement of both groups. Arts majors＇ motivation depends heavily on their attitude towards English while science majors are instrumentally motivated to learn English. We also discuss some possible reasons for the differences in motivation and implications of the study for ESL teachers and learners.
基金We are grateful for financial support from the National Key Research and Development Plan(2019YFC1805100)the National Natural Science Foundation of China(42107341,21767012)+2 种基金the UK Natural Environment Research Council and the Global Challenges Research Fund(NE/V005871/1)sponsorship by K.C.Wong Magna Fund in Ningbo Universitythe Science and Technology Plan of Ganzhou City(GSKF201850).
文摘Microplastics provide a new ecological niche for microorganisms,and the accumulation levels of microplastics(MPs)in terrestrial ecosystems are higher than those in marine ecosystems.Here,we applied the zymography to investigate how MPs–polyethylene[PE],and polyvinyl chloride[PVC])at two levels(0.01%and 1%soil weight)impacted the spatial distribution of soil hydrolases,nutrient availability,and rice growth in paddy soil.MPs increased the above-ground biomass by 13.0%–15.5%and decreased the below-ground biomass by 8.0%–15.1%.Addition of 0.01%and 1%MPs reduced soil NH4+content by 18.3%–63.2%and 52.2%–80.2%,respectively.The average activities of N-and P-hydrolases increased by 0.8%–4.8%and 1.9%–6.3%with addition of MPs,respectively.The nutrient uptake by rice plants and the enzyme activities in hotspots increased with MP content in soil.The accumulation of MPs in paddy soil could provide an ecological niche that facilitates microbial survival,alters the spatial distribution of soil hydrolases,and decreases nutrient availability.
文摘Aggregate demand or supply at equilibrium is commonly used as a representative of the macroeconomic activity of an economy whereby aggregate demand denotes the behaviour of individuals and households.However,aggregate demand can also directly affect environmental deterioration via changes in aggregate production.This study tried to explore this relationship,known as the demand-based Environmental Kuznets Curve(Demand EKC)and the role of different knowledge economy indicators.Knowledge economy indicators are proposed to influence consumption patterns,altering the demand EKC that empirical studies have understudied.For this purpose,secondary data for 147 countries were collected from 2008 to 2018,also classified as development-wise.This study found that aggregate demand significantly affects carbon emissions.The long-run results are estimated using the Fully Modified Ordinary Least Square method.Controlling factors like renewable energy consumption,population density,and financial development significantly affect carbon emissions in sample countries.This study has incorporated four pillars of a knowledge-based economy and the results showed that these indicators helped reduce consumption-related CO_(2) emissions.
