Malaria is a significant global health challenge.This devastating disease continues to affect millions,especially in tropical regions.It is caused by Plasmodium parasites transmitted by female Anopheles mosquitoes.Thi...Malaria is a significant global health challenge.This devastating disease continues to affect millions,especially in tropical regions.It is caused by Plasmodium parasites transmitted by female Anopheles mosquitoes.This study introduces a nonlinear mathematical model for examining the transmission dynamics of malaria,incorporating both human and mosquito populations.We aim to identify the key factors driving the endemic spread of malaria,determine feasible solutions,and provide insights that lead to the development of effective prevention and management strategies.We derive the basic reproductive number employing the next-generation matrix approach and identify the disease-free and endemic equilibrium points.Stability analyses indicate that the disease-free equilibrium is locally and globally stable when the reproductive number is below one,whereas an endemic equilibrium persists when this threshold is exceeded.Sensitivity analysis identifies the most influential mosquito-related parameters,particularly the bite rate and mosquito mortality,in controlling the spread of malaria.Furthermore,we extend our model to include a treatment compartment and three disease-preventive control variables such as antimalaria drug treatments,use of larvicides,and the use of insecticide-treated mosquito nets for optimal control analysis.The results show that optimal use of mosquito nets,use of larvicides for mosquito population control,and treatment can lower the basic reproduction number and control malaria transmission with minimal intervention costs.The analysis of disease control strategies and findings offers valuable information for policymakers in designing cost-effective strategies to combat malaria.展开更多
Global Navigation Satellite System(GNSS)-based continuous and accurate train positioning is one of the key technologies for advanced train operations such as train virtual coupling.However,GNSS-based train positioning...Global Navigation Satellite System(GNSS)-based continuous and accurate train positioning is one of the key technologies for advanced train operations such as train virtual coupling.However,GNSS-based train positioning faces significant challenges in real-world scenarios due to environmental complexities and signal interferences.Considering this issue,this paper presents an approach for modeling and performance analysis of GNSS-based train positioning systems using Colored Petri Nets(CPNs).By systematically modeling the GNSS signal reception and processing process,the performance of the positioning system under various environment scenarios is evaluated.The system model integrates three types of interference signals(i.e.,Amplitude Modulation(AM)signals,Frequency Modulation(FM)signals,and pulse signals)while incorporating environmental factors such as terrain obstructions and tunnel shielding.Additionally,the Extended Kalman Filter(EKF)algorithm is employed to process GNSS observation data,providing accurate train position estimations.The simulation results demonstrate that signal interferences and complex environmental conditions significantly affect the GNSS-based positioning accuracy.This study offers a comprehensive framework for evaluating the performance of GNSS-based train positioning systems in different scenarios,highlighting critical factors that influence positioning accuracy and stability.展开更多
The paper primarily focuses on social safety nets and their effectiveness in poverty alleviation.Social Safety Net(SSN)programs pertain to social service initiatives aimed at providing temporary assistance to individu...The paper primarily focuses on social safety nets and their effectiveness in poverty alleviation.Social Safety Net(SSN)programs pertain to social service initiatives aimed at providing temporary assistance to individuals or groups facing vulnerabilities or unexpected hardships,such as those with lower incomes.Poverty poses a significant obstacle to the progress of social development,and its impacts are worsened by various factors including insecurity,frequent flooding,and droughts in Somalia.A total of 342 households in the Banadir region of Somalia were interviewed for the social safety nets(SSN)study.Data collection in the study was facilitated through the utilization of Kobo Toolbox,while the data analysis was conducted using EViews v.12.The results obtained from the ADP and PP tests indicated that all variables exhibited stationarity at the level.The Impact Assessment(IA)reveals a positive correlation with Household Income and Poverty Indices(HIPI),suggesting a risk of dependency without a strategic exit strategy,potentially leading to a 26%increase in poverty levels.A well-executed Program Implementation and Design(PID)can result in a 33%increase in income and poverty indices.Recipients perceive the Social Safety Net(PSSN)as reducing poverty and increasing income by 11%.Therefore,the study recommends integrating beneficiaries into the urban economy through sustainable livelihood options.Finally,the Somali government should prioritize the implementation of sustainable livelihood programs to mitigate dependency and alleviate poverty among SSN beneficiaries.展开更多
Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific ...Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.展开更多
Acute lung injury(ALI)linked to sepsis has a high mortality rate,with limited treatment options available.In recent studies,medical ozone has shown the potential to alleviate inflammation and infection.Here,we aimed t...Acute lung injury(ALI)linked to sepsis has a high mortality rate,with limited treatment options available.In recent studies,medical ozone has shown the potential to alleviate inflammation and infection.Here,we aimed to evaluate therapeutic potential of medical ozone in a mouse model of the sepsis-induced ALI by measuring behavioral assessments,lung function,and blood flow.Protein levels were quantified by Western blotting.In vitro,we performed experiments on bone marrow-derived macrophages(BMDMs)to investigate the effect of adenosine monophosphate(AMP)-activated protein kinase(AMPK)inhibitors and agonists on their phagocytic activity.The results showed that medical ozone significantly improved the survival rate,ameliorated lung injury,and enhanced lung function and limb microcirculation in mice with ALI.Notably,medical ozone inhibited the formation of neutrophil extracellular traps(NETs),a crucial factor in the ALI development.Additionally,medical ozone counteracted the elevated levels of tissue factor,matrix metalloproteinase-9,and interleukin-1β.In the ALI mice,the effects of ozone were abolished,and BMDMs showed an impaired capacity to engulf NETs following the Sr-a1 knockout.Under normal physiological conditions,the administration of an AMPK antagonist showed similar effects on the Sr-a1 knockout,significantly inhibiting the phagocytosis of NETs by BMDMs.In contrast,AMPK agonists enhanced this phagocytic process.In conclusion,medical ozone may alleviate the sepsis-induced lung injury through the AMPK/SR-A1 pathway,thereby enhancing the phagocytosis of NETs by macrophages.展开更多
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Grant No.KFU252959].
文摘Malaria is a significant global health challenge.This devastating disease continues to affect millions,especially in tropical regions.It is caused by Plasmodium parasites transmitted by female Anopheles mosquitoes.This study introduces a nonlinear mathematical model for examining the transmission dynamics of malaria,incorporating both human and mosquito populations.We aim to identify the key factors driving the endemic spread of malaria,determine feasible solutions,and provide insights that lead to the development of effective prevention and management strategies.We derive the basic reproductive number employing the next-generation matrix approach and identify the disease-free and endemic equilibrium points.Stability analyses indicate that the disease-free equilibrium is locally and globally stable when the reproductive number is below one,whereas an endemic equilibrium persists when this threshold is exceeded.Sensitivity analysis identifies the most influential mosquito-related parameters,particularly the bite rate and mosquito mortality,in controlling the spread of malaria.Furthermore,we extend our model to include a treatment compartment and three disease-preventive control variables such as antimalaria drug treatments,use of larvicides,and the use of insecticide-treated mosquito nets for optimal control analysis.The results show that optimal use of mosquito nets,use of larvicides for mosquito population control,and treatment can lower the basic reproduction number and control malaria transmission with minimal intervention costs.The analysis of disease control strategies and findings offers valuable information for policymakers in designing cost-effective strategies to combat malaria.
基金supported by the National Key Research and Development Program of China(2023YFB3907300)the Fundamental Research Funds for the Central Universities(2024JBMC002)the National Natural Science Foundation of China(T2222015,U2268206).
文摘Global Navigation Satellite System(GNSS)-based continuous and accurate train positioning is one of the key technologies for advanced train operations such as train virtual coupling.However,GNSS-based train positioning faces significant challenges in real-world scenarios due to environmental complexities and signal interferences.Considering this issue,this paper presents an approach for modeling and performance analysis of GNSS-based train positioning systems using Colored Petri Nets(CPNs).By systematically modeling the GNSS signal reception and processing process,the performance of the positioning system under various environment scenarios is evaluated.The system model integrates three types of interference signals(i.e.,Amplitude Modulation(AM)signals,Frequency Modulation(FM)signals,and pulse signals)while incorporating environmental factors such as terrain obstructions and tunnel shielding.Additionally,the Extended Kalman Filter(EKF)algorithm is employed to process GNSS observation data,providing accurate train position estimations.The simulation results demonstrate that signal interferences and complex environmental conditions significantly affect the GNSS-based positioning accuracy.This study offers a comprehensive framework for evaluating the performance of GNSS-based train positioning systems in different scenarios,highlighting critical factors that influence positioning accuracy and stability.
文摘The paper primarily focuses on social safety nets and their effectiveness in poverty alleviation.Social Safety Net(SSN)programs pertain to social service initiatives aimed at providing temporary assistance to individuals or groups facing vulnerabilities or unexpected hardships,such as those with lower incomes.Poverty poses a significant obstacle to the progress of social development,and its impacts are worsened by various factors including insecurity,frequent flooding,and droughts in Somalia.A total of 342 households in the Banadir region of Somalia were interviewed for the social safety nets(SSN)study.Data collection in the study was facilitated through the utilization of Kobo Toolbox,while the data analysis was conducted using EViews v.12.The results obtained from the ADP and PP tests indicated that all variables exhibited stationarity at the level.The Impact Assessment(IA)reveals a positive correlation with Household Income and Poverty Indices(HIPI),suggesting a risk of dependency without a strategic exit strategy,potentially leading to a 26%increase in poverty levels.A well-executed Program Implementation and Design(PID)can result in a 33%increase in income and poverty indices.Recipients perceive the Social Safety Net(PSSN)as reducing poverty and increasing income by 11%.Therefore,the study recommends integrating beneficiaries into the urban economy through sustainable livelihood options.Finally,the Somali government should prioritize the implementation of sustainable livelihood programs to mitigate dependency and alleviate poverty among SSN beneficiaries.
基金Project(2020YFC2008605)supported by the National Key Research and Development Project of ChinaProject(52072412)supported by the National Natural Science Foundation of ChinaProject(2021JJ30359)supported by the Natural Science Foundation of Hunan Province,China。
文摘Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.
基金supported by the National Natural Science Foundation of China(Grant Nos.82271252,82204542,and 81971047)the Lianyungang Science and Technology Program Project(Grant Nos.SF2122 and SF2214)+2 种基金the Scientific Research Project of Jiangsu Provincial Health Commission(Grant No.Z2021066)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.21KJB310019)the Open Project of Jiangsu Province Key Laboratory of Anesthesiology,Xuzhou Medical University(Grant No.XZSYSKF2021014).
文摘Acute lung injury(ALI)linked to sepsis has a high mortality rate,with limited treatment options available.In recent studies,medical ozone has shown the potential to alleviate inflammation and infection.Here,we aimed to evaluate therapeutic potential of medical ozone in a mouse model of the sepsis-induced ALI by measuring behavioral assessments,lung function,and blood flow.Protein levels were quantified by Western blotting.In vitro,we performed experiments on bone marrow-derived macrophages(BMDMs)to investigate the effect of adenosine monophosphate(AMP)-activated protein kinase(AMPK)inhibitors and agonists on their phagocytic activity.The results showed that medical ozone significantly improved the survival rate,ameliorated lung injury,and enhanced lung function and limb microcirculation in mice with ALI.Notably,medical ozone inhibited the formation of neutrophil extracellular traps(NETs),a crucial factor in the ALI development.Additionally,medical ozone counteracted the elevated levels of tissue factor,matrix metalloproteinase-9,and interleukin-1β.In the ALI mice,the effects of ozone were abolished,and BMDMs showed an impaired capacity to engulf NETs following the Sr-a1 knockout.Under normal physiological conditions,the administration of an AMPK antagonist showed similar effects on the Sr-a1 knockout,significantly inhibiting the phagocytosis of NETs by BMDMs.In contrast,AMPK agonists enhanced this phagocytic process.In conclusion,medical ozone may alleviate the sepsis-induced lung injury through the AMPK/SR-A1 pathway,thereby enhancing the phagocytosis of NETs by macrophages.
