The aviation industry has seen significant advancements in safety procedures over the past few decades, resulting in a steady decline in aviation deaths worldwide. However, the safety standards in General Aviation (GA...The aviation industry has seen significant advancements in safety procedures over the past few decades, resulting in a steady decline in aviation deaths worldwide. However, the safety standards in General Aviation (GA) are still lower compared to those in commercial aviation. With the anticipated growth in air travel, there is an imminent need to improve operational safety in GA. One way to improve aircraft and operational safety is through trajectory prediction. Trajectory prediction plays a key role in optimizing air traffic control and improving overall flight safety. This paper proposes a meta-learning approach to predict short- to mid-term trajectories of aircraft using historical real flight data collected from multiple GA aircraft. The proposed solution brings together multiple models to improve prediction accuracy. In this paper, we are combining two models, Random Forest Regression (RFR) and Long Short-term Memory (LSTM), using k-Nearest Neighbors (k-NN), to output the final prediction based on the combined output of the individual models. This approach gives our model an edge over single-model predictions. We present the results of our meta-learner and evaluate its performance against individual models using the Mean Absolute Error (MAE), Absolute Altitude Error (AAE), and Root Mean Squared Error (RMSE) evaluation metrics. The proposed methodology for aircraft trajectory forecasting is discussed in detail, accompanied by a literature review and an overview of the data preprocessing techniques used. The results demonstrate that the proposed meta-learner outperforms individual models in terms of accuracy, providing a more robust and proactive approach to improve operational safety in GA.展开更多
Neuroblastoma(NB),which is the most common pediatric extracranial solid tumor,varies widely in its clinical presentation and outcome.NB has a unique ability to spontaneously differentiate and regress,suggesting a pote...Neuroblastoma(NB),which is the most common pediatric extracranial solid tumor,varies widely in its clinical presentation and outcome.NB has a unique ability to spontaneously differentiate and regress,suggesting a potential direction for therapeutic intervention.However,the underlying mechanisms of regression remain largely unknown,and more reliable prognostic biomarkers are needed for predicting trajectories for NB.We performed scRNAseq analysis on 17 NB clinical samples and three peritumoral adrenal tissues.Primary NB displayed varied cell constitution,even among tumors of the same pathological subtype.Copy number variation patterns suggested that neuroendocrine cells represent the malignant cell type.Based on the differential expression of sets of related marker genes,a subgroup of neuroendocrine cells was identified and projected to differentiate into a subcluster of benign fibroblasts with highly expressed CCL2 and ZFP36,supporting a progressive pathway of spontaneous NB regression.We also identified prognostic markers(STMN2,TUBA1A,PAGE5,and ETV1)by evaluating intra-tumoral heterogeneity.Lastly,we determined that ITGB1 in M2-like macrophages was associated with favorable prognosis and may serve as a potential diagnostic marker and therapeutic target.In conclusion,our findings reveal novel mechanisms underlying regression and potential prognostic markers and therapeutic targets of NB.展开更多
In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting ...In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting impact point is established;secondly,the particle swarm algorithm（PSD）is used to optimize the smooth factor in the prediction model and then the optimal GRNN impact point prediction model is obtained.Finally,the numerical simulation of this prediction model is carried out.Simulation results show that the maximum range error is no more than 40 m,and the lateral deviation error is less than0.2m.The average time of impact point prediction is 6.645 ms,which is 1 300.623 ms less than that of numerical integration method.Therefore,it is feasible and effective for the proposed method to forecast projectile impact points,and thus it can provide a theoretical reference for practical engineering applications.展开更多
文摘The aviation industry has seen significant advancements in safety procedures over the past few decades, resulting in a steady decline in aviation deaths worldwide. However, the safety standards in General Aviation (GA) are still lower compared to those in commercial aviation. With the anticipated growth in air travel, there is an imminent need to improve operational safety in GA. One way to improve aircraft and operational safety is through trajectory prediction. Trajectory prediction plays a key role in optimizing air traffic control and improving overall flight safety. This paper proposes a meta-learning approach to predict short- to mid-term trajectories of aircraft using historical real flight data collected from multiple GA aircraft. The proposed solution brings together multiple models to improve prediction accuracy. In this paper, we are combining two models, Random Forest Regression (RFR) and Long Short-term Memory (LSTM), using k-Nearest Neighbors (k-NN), to output the final prediction based on the combined output of the individual models. This approach gives our model an edge over single-model predictions. We present the results of our meta-learner and evaluate its performance against individual models using the Mean Absolute Error (MAE), Absolute Altitude Error (AAE), and Root Mean Squared Error (RMSE) evaluation metrics. The proposed methodology for aircraft trajectory forecasting is discussed in detail, accompanied by a literature review and an overview of the data preprocessing techniques used. The results demonstrate that the proposed meta-learner outperforms individual models in terms of accuracy, providing a more robust and proactive approach to improve operational safety in GA.
基金This work was supported in part by research grants from the Key Project of“Research on Prevention and Control of Major Chronic Non-Communicable Diseases”,the Ministry of Science and Technology of the People’s Republic of China,National Key R&D Program of China(No.2018YFC1313000,2018YFC1313004)the General Clinical Medical Research Program of Children’s Hospital of Chongqing Medical University(No.YBXM-2019-003)the Chongqing Science and Technology Commission(No.cstc2019jscxmsxmX0220).
文摘Neuroblastoma(NB),which is the most common pediatric extracranial solid tumor,varies widely in its clinical presentation and outcome.NB has a unique ability to spontaneously differentiate and regress,suggesting a potential direction for therapeutic intervention.However,the underlying mechanisms of regression remain largely unknown,and more reliable prognostic biomarkers are needed for predicting trajectories for NB.We performed scRNAseq analysis on 17 NB clinical samples and three peritumoral adrenal tissues.Primary NB displayed varied cell constitution,even among tumors of the same pathological subtype.Copy number variation patterns suggested that neuroendocrine cells represent the malignant cell type.Based on the differential expression of sets of related marker genes,a subgroup of neuroendocrine cells was identified and projected to differentiate into a subcluster of benign fibroblasts with highly expressed CCL2 and ZFP36,supporting a progressive pathway of spontaneous NB regression.We also identified prognostic markers(STMN2,TUBA1A,PAGE5,and ETV1)by evaluating intra-tumoral heterogeneity.Lastly,we determined that ITGB1 in M2-like macrophages was associated with favorable prognosis and may serve as a potential diagnostic marker and therapeutic target.In conclusion,our findings reveal novel mechanisms underlying regression and potential prognostic markers and therapeutic targets of NB.
基金Project Funded by Chongqing Changjiang Electrical Appliances Industries Group Co.,Ltd
文摘In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting impact point is established;secondly,the particle swarm algorithm（PSD）is used to optimize the smooth factor in the prediction model and then the optimal GRNN impact point prediction model is obtained.Finally,the numerical simulation of this prediction model is carried out.Simulation results show that the maximum range error is no more than 40 m,and the lateral deviation error is less than0.2m.The average time of impact point prediction is 6.645 ms,which is 1 300.623 ms less than that of numerical integration method.Therefore,it is feasible and effective for the proposed method to forecast projectile impact points,and thus it can provide a theoretical reference for practical engineering applications.
