Purpose: The prevalence of acute ischemic stroke (AIS) is increasing among people of working age, posing socioeconomic and healthcare challenges. Inability to return to work can have significant negative consequences and contribute to the economic burden of stroke. Endovascular treatment (EVT) has been established as the standard of care for large vessel occlusion AIS patients. In this study, we aimed to identify factors predicting favorable outcome among working age AIS patients undergoing EVT. Patients and Methods: We analyzed data from 309 patients from five comprehensive stroke centers between 2019 and 2023. All patients were working age (18< 59) with symptoms onset of within 24 hours. Modified thrombolysis in cerebral infarction (mTICI) score of 2b-3 was considered as successful recanalization. We used 3-months mRS post-EVT to evaluate the outcome; mRS of 0– 2 was defined as favorable outcome, whereas mRS of 3– 6 was considered unfavorable outcome. Results: The unfavorable outcome group consisted of 150 patients, and 159 patients were in the favorable outcome group. More people in the unfavorable outcome group had diabetes (29.33% vs 15.72%, p=0.004) and hypertension (61.33% vs 40.88, p< 0.001). A multivariable regression analysis demonstrated that several factors, including successful recanalization (odds ratio (OR) 5.298, 95% confidence interval (CI) 1.735– 16.174, p=0.003), pre-EVT NIHSS (OR 0.892, 95% CI 0.852– 0.934, p=0.000), baseline Alberta stroke program early CT score (ASPECTS) (OR 1.609, 95% CI 1.274– 2.032, p=0.000), hypertension (OR 0.477, 95% CI 0.270– 0.845, p=0.011), diabetes mellitus (OR 0.413, 95% CI 0.208– 0.820, p=0.011), and symptomatic intracerebral hemorrhage (sICH) (OR 0.045, 95% CI 0.006– 0.359, p=0.003) can predict the outcome of patients undergoing EVT. Conclusion: Favorable outcome of working age patients with AIS undergoing EVT can be predicted using multiple factors, including hypertension, diabetes mellitus, successful recanalization, pre-EVT NIHSS, baseline ASPECTS, and sICH.

Transmission tower lifting is a critical operation in power transmission construction, where posture control and load safety directly affect structural reliability and worker security. However, current field monitoring still relies heavily on manual observation and scattered single-sensor devices, which are difficult to deploy in complex environments and large-span hoisting tasks. These traditional approaches suffer from low measurement accuracy, delayed response to abnormal conditions, and limited capability for comprehensive safety assessment. To address these challenges, this study proposes an intelligent monitoring scheme for transmission tower lifting based on tightly coupled multi-sensor fusion. The system integrates multiple sensor measurements, including inertial measurement units (IMUs), to estimate tower posture, force distribution, and structural integrity throughout the lifting process. A Bayesian estimation-based multi-sensor fusion algorithm is developed to achieve real-time, high-precision reconstruction. Simulation results demonstrate that the system can accurately capture dynamic responses, identify abnormal working conditions at an early stage, and provide timely decision support for operators. Compared with traditional manual monitoring methods, significant improvements in monitoring accuracy, fault detection sensitivity, and operational efficiency were observed. This research offers practical technical support for the intelligent construction and safe operation of power transmission infrastructure.

In the rapidly evolving field of unmanned aerial vehicle (UAV) applications, the complexity of task planning and trajectory optimization, particularly in high-dimensional operational environments, is increasingly challenging. This study addresses these challenges by developing the Adaptive Distortion Suppression Correlation Filter Cooperative Optimization (ARCF-ICO) algorithm, designed for high-dimensional UAV task allocation and trajectory planning. The ARCF-ICO algorithm combines advanced correlation filter technologies with multi-objective optimization techniques, enhancing the precision of trajectory planning and efficiency of task allocation. By incorporating weather conditions and other environmental factors, the algorithm ensures robust performance at low altitudes. The ARCF-ICO algorithm improves UAV tracking stability and accuracy by suppressing distortions, facilitating optimal path selection and task execution. Experimental validation using the UAV123@10fps and OTB-100 datasets demonstrates that the ARCF-ICO algorithm outperforms existing methods in Area Under the Curve (AUC) and Precision metrics. Additionally, the algorithm’s consideration of battery consumption and endurance further validates its applicability to current UAV technologies. This research advances UAV mission planning and sets new standards for UAV deployment in both civilian and military applications, where adaptability and accuracy are critical.

The integration of Unmanned Aerial Vehicles (UAVs) with electronic nautical charts represents a significant advancement in maritime navigation assistance, yet existing solutions face critical limitations in real-time positioning accuracy and data transmission efficiency over marine environments. This study addresses these challenges by proposing a novel multi-source data fusion framework that integrates UAV telemetry data with S-100 electronic nautical charts through a unified Maritime Navigation Digital Service Platform. The proposed system employs a hybrid positioning architecture combining GPS/BeiDou satellite navigation with Automatic Identification System (AIS) terminals, enabling real-time position broadcasting at sub-meter accuracy. We developed a weighted least-squares fusion algorithm that optimally combines multiple positioning sources while accounting for time-varying measurement uncertainties. Field experiments conducted across four maritime locations using DJI Matrice and Mavic series UAVs demonstrated horizontal positioning accuracy of 0.18±0.06 m and data transmission latency of 125±15 ms through the SDK API interface. The integration success rate exceeded 97% across all tested UAV models over a 30-day evaluation period. This research provides a practical solution for enhancing maritime navigation aid inspection, search and rescue operations, and vessel traffic monitoring through intelligent UAV-ENC integration.