The Harlem River, a 9.3-mile channel that flows from the Hudson River to the East River, has experienced decades of industrial abuse and remains gritty and industrial. During heavy rains, the pipes discharge raw sewag...The Harlem River, a 9.3-mile channel that flows from the Hudson River to the East River, has experienced decades of industrial abuse and remains gritty and industrial. During heavy rains, the pipes discharge raw sewage into the river through combined sewer overflows (CSOs) that can contain bacteria and cause illness. Water samples were collected from CSO discharge point and several adjacent sites along the river in the Bronx side close to River Park Towers at Richman Plaza and Manhattan side at Wards Island. Nutrients, bacteria, polychlorinated biphenyls (PCBs), and fish consumption safety have been analyzed. Results showed that phosphorus, ammonia concentration as well as fecal coliform, E.Coli, enterococcus levels increased significantly during heavy rainstorms. Ammonia concentration was up to 2.725 mg/L during tropical storm Arthur on July 2, 2014 and rainstorm in May 2013, and soluble reactive phosphorus (SRP) or orthophosphate was up to 0.197 mg/L during heavy thunderstorm in April 2011;both nutrients were exceeded EPA regulation for ammonia (0.23 mg/L) and phosphate (0.033 mg/L) for New York City (NYC) waters. The colonies of fecal coliform were more than 5 million MPN/100ml (most probable number per 100 ml) during tropical storm Arthur in July 2014 and heavy rainstorm in April 2014, and fecal coliform was more than 10,000 MPN/100ml during storm in July and November 2013;E.Coli reached more than 5000 MPN/100ml during tropical storm Arthur and storm in May 2013;enterococcus reached more than 10,000 MPN/100ml during tropical storm Arthur and heavy rainstorm in April 2014. These bacteria (pathogen) levels in the Harlem River were significantly higher than EPA standards (fecal coliform: 200 MPN/100ml, E.Coli: 126 MPN/100ml, enterococcus: 104 MPN/100ml), especially during rainstorm/tropical storm. Of particular significance, nutrients and bacteria were analyzed before and after Hurricane Sandy devastated NYC in late October 2012;results determined that bacteria and ammonia concentrations increased after this monumental storm, elucidating the environmental impact of large storm events. PCB 11 (3,3’-dichlorobiphenyl, C12H8Cl2), the high molecular weight (MW), an indicator of raw sewer and storm water runoff in the NYC harbor waters, is the major polychlorinated biphenyls (PCBs) in the Harlem River. PCBs are carcinogenic, which could bioaccumulate via food chain from fish and seafood, endangering public health. Oyster farming has been used to purify water and improve water quality in the river. CSOs and storm water runoff have degraded water quality and been threatening environmental ecosystem and public health. This research will help local communities understand CSO impact on nutrients, bacteria, PCBs contamination and fish consumption safety, and make contributions on CSOs reduction as well as improve water quality and environmental ecosystem in the Harlem River.展开更多
Background: The importance of civil society organisations in health care delivery systems cannot be under-rated in sub-SaharanAfricaand other developing nations worldwide. Civil society organisations play a central ro...Background: The importance of civil society organisations in health care delivery systems cannot be under-rated in sub-SaharanAfricaand other developing nations worldwide. Civil society organisations play a central role in service delivery and development of democracy. However, little is known about the roles and achievements of Civil Society Organisations (CSOs) in healthcare. The study aimed at exploring the role of civil society organisations in health care delivery system particularly in children immunization. Methods: A questionnaire survey involving 282 households was conducted. Data were analysed using descriptive statistics followed by multivariable logistic regression. Results: Ninety seven percent (97%) confirmed that CSOs/NGOs healthcare facilities played a major role in healthcare service delivery. 84% travelled long distances to access the healthcare services including child immunisation services. Travelling long distances (>2 km) to access for health care services including immunization compared to short distance (<1 km) (OR = 0.4, P = 0.0001), possessing a food stores (enguli) compared to not having a food store (enguli) (OR = 2.3, P = 0.002), having separate animal houses compared to not having livestock houses (OR = 0.09, P = 0.0001), and owning a bicycle compared to not having a bicycle (OR = 2.2, P = 0.005) are important determinants for the number of clients at CSO health centers. Further, possessing and using a cellphone compared to no cellphone (OR = 3.7, P = 0.001), Possessing and watching a television compared to not having a television (OR = 2.4, P = 0.002), educated compared to not acquiring any formal education (OR = 0.084, P = 0.0001), and female compared to male respondent (OR = 0.49, P = 0.0045) are other most important factors likely to determine the numbers of clients at the CSO healthcare. Conclusion: Geographies of CSO and community socioeconomics strongly determine the operations and the roles played by the CSO healthcare services in Uganda. Further studies to assess the role of CSO health service providers in other healthcare services need to be done.展开更多
The rapid development of military technology has prompted different types of equipment to break the limits of operational domains and emerged through complex interactions to form a vast combat system of systems(CSoS),...The rapid development of military technology has prompted different types of equipment to break the limits of operational domains and emerged through complex interactions to form a vast combat system of systems(CSoS),which can be abstracted as a heterogeneous combat network(HCN).It is of great military significance to study the disintegration strategy of combat networks to achieve the breakdown of the enemy’s CSoS.To this end,this paper proposes an integrated framework called HCN disintegration based on double deep Q-learning(HCN-DDQL).Firstly,the enemy’s CSoS is abstracted as an HCN,and an evaluation index based on the capability and attack costs of nodes is proposed.Meanwhile,a mathematical optimization model for HCN disintegration is established.Secondly,the learning environment and double deep Q-network model of HCN-DDQL are established to train the HCN’s disintegration strategy.Then,based on the learned HCN-DDQL model,an algorithm for calculating the HCN’s optimal disintegration strategy under different states is proposed.Finally,a case study is used to demonstrate the reliability and effectiveness of HCNDDQL,and the results demonstrate that HCN-DDQL can disintegrate HCNs more effectively than baseline methods.展开更多
采用暴雨管理模型(Storm Water Management Model,SWMM)对柳州市竹鹅溪片区截流式合流制管网溢流进行了水质水量模拟,并将模拟结果与实测数据进行了比较分析。结果表明,虽然TSS模拟结果与实测值偏差较大,但模型的水量模拟具有较高的可...采用暴雨管理模型(Storm Water Management Model,SWMM)对柳州市竹鹅溪片区截流式合流制管网溢流进行了水质水量模拟,并将模拟结果与实测数据进行了比较分析。结果表明,虽然TSS模拟结果与实测值偏差较大,但模型的水量模拟具有较高的可靠性与准确性,TN、TP、COD三种污染物模拟结果也与实测数据偏差较小。SWMM模型可以成为城市面源污染研究的有效工具,并为合流制溢流污染控制提供科学参考。展开更多
Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate becau...Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators.These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult,and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles.Modern medical datasets’complexity and high dimensionality challenge traditional predictionmodels like SupportVectorMachines and Decision Trees.Quantum approaches include QSVM,QkNN,QDT,and others.These Constraints drove research.The“QHF-CS:Quantum-Enhanced Heart Failure Prediction using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data”system was developed in this research.This novel system leverages a Quantum Convolutional Neural Network(QCNN)-based quantum circuit,enhanced by meta-heuristic algorithms—Cuckoo SearchOptimization(CSO),Artificial BeeColony(ABC),and Particle SwarmOptimization(PSO)—for feature qubit selection.Among these,CSO demonstrated superior performance by consistently identifying the most optimal and least skewed feature subsets,which were then encoded into quantum states for circuit construction.By integrating advanced quantum circuit feature maps like ZZFeatureMap,RealAmplitudes,and EfficientSU2,the QHF-CS model efficiently processes complex,high-dimensional data,capturing intricate patterns that classical models overlook.The QHF-CS model improves precision,recall,F1-score,and accuracy to 0.94,0.95,0.94,and 0.94.Quantum computing could revolutionize heart failure diagnostics by improving model accuracy and computational efficiency,enabling complex healthcare diagnostic breakthroughs.展开更多
文摘The Harlem River, a 9.3-mile channel that flows from the Hudson River to the East River, has experienced decades of industrial abuse and remains gritty and industrial. During heavy rains, the pipes discharge raw sewage into the river through combined sewer overflows (CSOs) that can contain bacteria and cause illness. Water samples were collected from CSO discharge point and several adjacent sites along the river in the Bronx side close to River Park Towers at Richman Plaza and Manhattan side at Wards Island. Nutrients, bacteria, polychlorinated biphenyls (PCBs), and fish consumption safety have been analyzed. Results showed that phosphorus, ammonia concentration as well as fecal coliform, E.Coli, enterococcus levels increased significantly during heavy rainstorms. Ammonia concentration was up to 2.725 mg/L during tropical storm Arthur on July 2, 2014 and rainstorm in May 2013, and soluble reactive phosphorus (SRP) or orthophosphate was up to 0.197 mg/L during heavy thunderstorm in April 2011;both nutrients were exceeded EPA regulation for ammonia (0.23 mg/L) and phosphate (0.033 mg/L) for New York City (NYC) waters. The colonies of fecal coliform were more than 5 million MPN/100ml (most probable number per 100 ml) during tropical storm Arthur in July 2014 and heavy rainstorm in April 2014, and fecal coliform was more than 10,000 MPN/100ml during storm in July and November 2013;E.Coli reached more than 5000 MPN/100ml during tropical storm Arthur and storm in May 2013;enterococcus reached more than 10,000 MPN/100ml during tropical storm Arthur and heavy rainstorm in April 2014. These bacteria (pathogen) levels in the Harlem River were significantly higher than EPA standards (fecal coliform: 200 MPN/100ml, E.Coli: 126 MPN/100ml, enterococcus: 104 MPN/100ml), especially during rainstorm/tropical storm. Of particular significance, nutrients and bacteria were analyzed before and after Hurricane Sandy devastated NYC in late October 2012;results determined that bacteria and ammonia concentrations increased after this monumental storm, elucidating the environmental impact of large storm events. PCB 11 (3,3’-dichlorobiphenyl, C12H8Cl2), the high molecular weight (MW), an indicator of raw sewer and storm water runoff in the NYC harbor waters, is the major polychlorinated biphenyls (PCBs) in the Harlem River. PCBs are carcinogenic, which could bioaccumulate via food chain from fish and seafood, endangering public health. Oyster farming has been used to purify water and improve water quality in the river. CSOs and storm water runoff have degraded water quality and been threatening environmental ecosystem and public health. This research will help local communities understand CSO impact on nutrients, bacteria, PCBs contamination and fish consumption safety, and make contributions on CSOs reduction as well as improve water quality and environmental ecosystem in the Harlem River.
文摘Background: The importance of civil society organisations in health care delivery systems cannot be under-rated in sub-SaharanAfricaand other developing nations worldwide. Civil society organisations play a central role in service delivery and development of democracy. However, little is known about the roles and achievements of Civil Society Organisations (CSOs) in healthcare. The study aimed at exploring the role of civil society organisations in health care delivery system particularly in children immunization. Methods: A questionnaire survey involving 282 households was conducted. Data were analysed using descriptive statistics followed by multivariable logistic regression. Results: Ninety seven percent (97%) confirmed that CSOs/NGOs healthcare facilities played a major role in healthcare service delivery. 84% travelled long distances to access the healthcare services including child immunisation services. Travelling long distances (>2 km) to access for health care services including immunization compared to short distance (<1 km) (OR = 0.4, P = 0.0001), possessing a food stores (enguli) compared to not having a food store (enguli) (OR = 2.3, P = 0.002), having separate animal houses compared to not having livestock houses (OR = 0.09, P = 0.0001), and owning a bicycle compared to not having a bicycle (OR = 2.2, P = 0.005) are important determinants for the number of clients at CSO health centers. Further, possessing and using a cellphone compared to no cellphone (OR = 3.7, P = 0.001), Possessing and watching a television compared to not having a television (OR = 2.4, P = 0.002), educated compared to not acquiring any formal education (OR = 0.084, P = 0.0001), and female compared to male respondent (OR = 0.49, P = 0.0045) are other most important factors likely to determine the numbers of clients at the CSO healthcare. Conclusion: Geographies of CSO and community socioeconomics strongly determine the operations and the roles played by the CSO healthcare services in Uganda. Further studies to assess the role of CSO health service providers in other healthcare services need to be done.
基金supported by the National Natural Science Foundation of China(7200120972231011+2 种基金72071206)the Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province(2020RC4046)the Science Foundation for Outstanding Youth Scholars of Hunan Province(2022JJ20047).
文摘The rapid development of military technology has prompted different types of equipment to break the limits of operational domains and emerged through complex interactions to form a vast combat system of systems(CSoS),which can be abstracted as a heterogeneous combat network(HCN).It is of great military significance to study the disintegration strategy of combat networks to achieve the breakdown of the enemy’s CSoS.To this end,this paper proposes an integrated framework called HCN disintegration based on double deep Q-learning(HCN-DDQL).Firstly,the enemy’s CSoS is abstracted as an HCN,and an evaluation index based on the capability and attack costs of nodes is proposed.Meanwhile,a mathematical optimization model for HCN disintegration is established.Secondly,the learning environment and double deep Q-network model of HCN-DDQL are established to train the HCN’s disintegration strategy.Then,based on the learned HCN-DDQL model,an algorithm for calculating the HCN’s optimal disintegration strategy under different states is proposed.Finally,a case study is used to demonstrate the reliability and effectiveness of HCNDDQL,and the results demonstrate that HCN-DDQL can disintegrate HCNs more effectively than baseline methods.
文摘采用暴雨管理模型(Storm Water Management Model,SWMM)对柳州市竹鹅溪片区截流式合流制管网溢流进行了水质水量模拟,并将模拟结果与实测数据进行了比较分析。结果表明,虽然TSS模拟结果与实测值偏差较大,但模型的水量模拟具有较高的可靠性与准确性,TN、TP、COD三种污染物模拟结果也与实测数据偏差较小。SWMM模型可以成为城市面源污染研究的有效工具,并为合流制溢流污染控制提供科学参考。
文摘Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators.These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult,and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles.Modern medical datasets’complexity and high dimensionality challenge traditional predictionmodels like SupportVectorMachines and Decision Trees.Quantum approaches include QSVM,QkNN,QDT,and others.These Constraints drove research.The“QHF-CS:Quantum-Enhanced Heart Failure Prediction using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data”system was developed in this research.This novel system leverages a Quantum Convolutional Neural Network(QCNN)-based quantum circuit,enhanced by meta-heuristic algorithms—Cuckoo SearchOptimization(CSO),Artificial BeeColony(ABC),and Particle SwarmOptimization(PSO)—for feature qubit selection.Among these,CSO demonstrated superior performance by consistently identifying the most optimal and least skewed feature subsets,which were then encoded into quantum states for circuit construction.By integrating advanced quantum circuit feature maps like ZZFeatureMap,RealAmplitudes,and EfficientSU2,the QHF-CS model efficiently processes complex,high-dimensional data,capturing intricate patterns that classical models overlook.The QHF-CS model improves precision,recall,F1-score,and accuracy to 0.94,0.95,0.94,and 0.94.Quantum computing could revolutionize heart failure diagnostics by improving model accuracy and computational efficiency,enabling complex healthcare diagnostic breakthroughs.
