The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication e...The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.展开更多
With the vast advancements in Information Technology,the emergence of Online Social Networking(OSN)has also hit its peak and captured the atten-tion of the young generation people.The clone intends to replicate the us...With the vast advancements in Information Technology,the emergence of Online Social Networking(OSN)has also hit its peak and captured the atten-tion of the young generation people.The clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original user.However,the attackers also target this height of OSN utilization,explicitly creating the clones of the user’s account.Various clone detection mechanisms are designed based on social-network activities.For instance,monitoring the occur-rence of clone edges is done to restrict the generation of clone activities.However,this assumption is unsuitable for a real-time environment and works optimally during the simulation process.This research concentrates on modeling and effi-cient clone prediction and avoidance methods to help the social network activists and the victims enhance the clone prediction accuracy.This model does not rely on assumptions.Here,an ensemble Adaptive Random Subspace is used for clas-sifying the clone victims with k-Nearest Neighbour(k-NN)as a base classifier.The weighted clone nodes are analysed using the weighted graph theory concept based on the classified results.When the weighted node’s threshold value is high-er,the trust establishment is terminated,and the clones are ranked and sorted in the higher place for termination.Thus,the victims are alert to the clone propaga-tion over the online social networking end,and the validation is done using the MATLAB 2020a simulation environment.The model shows a better trade-off than existing approaches like Random Forest(RF),Naïve Bayes(NB),and the standard graph model.Various performance metrics like True Positive Rate(TPR),False Alarm Rate(FAR),Recall,Precision,F-measure,and ROC and run time analysis are evaluated to show the significance of the model.展开更多
目的研究预见性护理措施在经皮微创胸腔置管闭式引流术后结核性渗出性胸膜炎患者中的应用效果。方法选择2016年6月至2018年6月在安康市中心医院传染科诊治的结核性渗出性胸膜炎患者90例进行研究,按照随机数表法分为观察组和对照组,每组4...目的研究预见性护理措施在经皮微创胸腔置管闭式引流术后结核性渗出性胸膜炎患者中的应用效果。方法选择2016年6月至2018年6月在安康市中心医院传染科诊治的结核性渗出性胸膜炎患者90例进行研究,按照随机数表法分为观察组和对照组,每组45例。所有患者均经皮微创胸腔置管闭式引流术治疗,对照组采用常规护理措施,观察组采用预见性护理措施。比较两组患者的住院时间、胸水吸收时间、血沉恢复正常时间、护理前后的生活质量问卷(QOLI)评分、患者对护理的满意度及并发症发生情况。结果护理后,观察组和对照组患者的住院时间[(16.72±5.54) d vs (23.86±7.38) d]、胸水吸收时间[(10.81±3.23) d vs (14.65±4.17) d]、血沉恢复正常时间[(13.68±3.46) d vs (16.69±5.22) d]和体温恢复正常时间[(2.16±0.65) d vs (3.97±1.06) d]比较,观察组明显短于对照组,差异均有统计学意义(P<0.05);护理前,两组患者的生活质量评分比较差异均无统计学意义(P>0.05);护理后,观察组和对照组患者的物质生活状态评分[(90.59±1.56)分vs (84.27±1.22)分]、躯体功能评分[(92.43±2.15)分vs (87.32±1.32)分]、社会功能评分[(92.16±2.18)分vs (85.33±1.13)分]和心理功能评分[(90.52±2.09)分vs (83.37±1.03)分]比较,观察组明显高于对照组,差异均有统计学意义(P<0.05);观察组患者对护理的满意度为95.56%,明显高于对照组的77.78%,差异有统计学意义(P<0.05);观察组患者的并发症总发生率为8.89%,明显低于对照组的33.33%,差异有统计学意义(P<0.05)。结论预见性护理措施对经皮微创胸腔置管闭式引流术后结核性渗出性胸膜炎患者的并发症预防效果显著,且能改善患者生活质量,值得临床推广应用。展开更多
文摘The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.
文摘With the vast advancements in Information Technology,the emergence of Online Social Networking(OSN)has also hit its peak and captured the atten-tion of the young generation people.The clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original user.However,the attackers also target this height of OSN utilization,explicitly creating the clones of the user’s account.Various clone detection mechanisms are designed based on social-network activities.For instance,monitoring the occur-rence of clone edges is done to restrict the generation of clone activities.However,this assumption is unsuitable for a real-time environment and works optimally during the simulation process.This research concentrates on modeling and effi-cient clone prediction and avoidance methods to help the social network activists and the victims enhance the clone prediction accuracy.This model does not rely on assumptions.Here,an ensemble Adaptive Random Subspace is used for clas-sifying the clone victims with k-Nearest Neighbour(k-NN)as a base classifier.The weighted clone nodes are analysed using the weighted graph theory concept based on the classified results.When the weighted node’s threshold value is high-er,the trust establishment is terminated,and the clones are ranked and sorted in the higher place for termination.Thus,the victims are alert to the clone propaga-tion over the online social networking end,and the validation is done using the MATLAB 2020a simulation environment.The model shows a better trade-off than existing approaches like Random Forest(RF),Naïve Bayes(NB),and the standard graph model.Various performance metrics like True Positive Rate(TPR),False Alarm Rate(FAR),Recall,Precision,F-measure,and ROC and run time analysis are evaluated to show the significance of the model.
文摘目的研究预见性护理措施在经皮微创胸腔置管闭式引流术后结核性渗出性胸膜炎患者中的应用效果。方法选择2016年6月至2018年6月在安康市中心医院传染科诊治的结核性渗出性胸膜炎患者90例进行研究,按照随机数表法分为观察组和对照组,每组45例。所有患者均经皮微创胸腔置管闭式引流术治疗,对照组采用常规护理措施,观察组采用预见性护理措施。比较两组患者的住院时间、胸水吸收时间、血沉恢复正常时间、护理前后的生活质量问卷(QOLI)评分、患者对护理的满意度及并发症发生情况。结果护理后,观察组和对照组患者的住院时间[(16.72±5.54) d vs (23.86±7.38) d]、胸水吸收时间[(10.81±3.23) d vs (14.65±4.17) d]、血沉恢复正常时间[(13.68±3.46) d vs (16.69±5.22) d]和体温恢复正常时间[(2.16±0.65) d vs (3.97±1.06) d]比较,观察组明显短于对照组,差异均有统计学意义(P<0.05);护理前,两组患者的生活质量评分比较差异均无统计学意义(P>0.05);护理后,观察组和对照组患者的物质生活状态评分[(90.59±1.56)分vs (84.27±1.22)分]、躯体功能评分[(92.43±2.15)分vs (87.32±1.32)分]、社会功能评分[(92.16±2.18)分vs (85.33±1.13)分]和心理功能评分[(90.52±2.09)分vs (83.37±1.03)分]比较,观察组明显高于对照组,差异均有统计学意义(P<0.05);观察组患者对护理的满意度为95.56%,明显高于对照组的77.78%,差异有统计学意义(P<0.05);观察组患者的并发症总发生率为8.89%,明显低于对照组的33.33%,差异有统计学意义(P<0.05)。结论预见性护理措施对经皮微创胸腔置管闭式引流术后结核性渗出性胸膜炎患者的并发症预防效果显著,且能改善患者生活质量,值得临床推广应用。