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PurposeTo evaluate the efficacy and safety of mFOLFOX-based hepatic arterial infusion chemotherapy (HAIC) combined with tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in the treatment of advanced hepatocellular carcinoma (HCC) with portal vein tumor thrombosis (PVTT)MethodsThis retrospective study included patients who received mFOLFOX-based HAIC combined with TKIs and ICIs from January 2021 to January 2023. The primary outcome was the objective response rate of PVTT response, and the secondary outcomes were 6-month, 1-year survival rate, overall survival (OS), and corresponding adverse events and complications were also evaluated. PVTT responses were assessed using ITK-SNAP software.ResultsA total of 37 patients were included in the analysis, 18.92% achieved a complete response and 56.76% achieved a partial response in PVTT response. The objective response rate (ORR) of PVTT was 75.68%. The 6-month survival rate was 89%, the 1-year survival rate was 66%, and the median OS was 15.8 months. In univariate analysis, Child-Pugh score (P=0.010) was important factor for predicting OS; in multivariate analysis, Child-Pugh score (P=0.015, HR= 3.089, 95%CI: 1.250–7.633) was the important factor for predicting OS. In terms of adverse reactions, the most common adverse reactions associated with HAIC are pain and thrombocytopenia associated with oxaliplatin.ConclusionFOLFOX-based HAIC combined with TKIs and ICIs induced an objective response rate of 75.68% in PVTT.Clinical signicanceFOLFOX-based HAIC combined with TKIs and ICIs provides more treatment options for PVTT.

Air Traffic Flow Management delay (ATFM delay) can quantitatively reflect the spatial and temporal distribution of congestion caused by the imbalance of airspace network capacity and demand, and is an important parameter to evaluate the effectiveness of air traffic flow management (ATFM) strategy. If the distribution of ATFM delay can be predicted in advance, the predictability and effectiveness of ATFM strategy can be improved. In this paper, we propose a short-time ATFM delay spatial and temporal distribution prediction method in the absence of historical records of specific ATFM delay causes. The method first clarifies the prediction object by constructing ATFM delay prediction network model, and proposes the ATFM delay prediction index system; Secondly, we use Information Value (IV) to filter and weight the features in order to construct a weighted ATFM delay prediction index system; Then, a support vector machine model based on particle swarm optimization(PSO-SVM) is implemented to predict ATFM delay distribution. Finally, some congested airports and key waypoints in China are selected as ATFM delay prediction network nodes for empirical analysis. The experimental results show that the prediction accuracy of the IV-PSO-SVM model reaches 96.4%, which is 13.5% and 9.1% higher than that of SVM model and PSO-SVM model, respectively.

Air Traffic Flow Management (ATFM) delay can quantitatively reflect the congestion caused by the imbalance between capacity and demand in an airspace network. Furthermore, it is an important parameter for the ex-post analysis of airspace congestion and the effectiveness of ATFM strategy implementation. If ATFM delays can be predicted in advance, the predictability and effectiveness of ATFM strategies can be improved. In this paper, a short-term ATFM delay regression prediction method is proposed for the characteristics of the multiple sources, high dimension, and complexity of ATFM delay prediction data. The method firstly constructs an ATFM delay prediction network model, specifies the prediction object, and proposes an ATFM delay prediction index system by integrating common flow control information. Secondly, an ATFM delay prediction method based on feature extraction modules (including CNN, TCN, and attention modules), a heuristic optimization algorithm (sparrow search algorithm (SSA)), and a prediction model (LSTM) are proposed. The method constructs a CNN-LSTM-ATT model based on SSA optimization and a TCN-LSTM-ATT model based on SSA optimization. Finally, four busy airports and their major waypoints in East China are selected as the ATFM delay prediction network nodes for example validation. The experimental results show that the MAEs of the two models proposed in this paper for ATFM delay regression prediction are 4.25 min and 4.38 min, respectively. Compared with the CNN-LSTM model, the errors are reduced by 2.71 min and 2.59 min, respectively. Compared with the TCN-LSTM model, the times are 3.68 min and 3.55 min, respectively. In this paper, two improved LSTM models are constructed to improve the prediction accuracy of ATFM delay duration so as to provide support for the establishment of an ATFM delay early warning mechanism, further improve ATFM delay management, and enhance resource allocation efficiency.