Many studies utilized machine learning schemes to improve network intrusion detection systems recently. Most of the research is based on manually extracted features, but this approach not only requires a lot of labor costs but also loses a lot of information in the original data, resulting in low judgment accuracy and cannot be deployed in actual situations. This paper develops a DL-IDS (deep learning-based intrusion detection system), which uses the hybrid network of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) to extract the spatial and temporal features of network traffic data and to provide a better intrusion detection system. To reduce the influence of an unbalanced number of samples of different attack types in model training samples on model performance, DL-IDS used a category weight optimization method to improve the robustness. Finally, DL-IDS is tested on CICIDS2017, a reliable intrusion detection dataset that covers all the common, updated intrusions and cyberattacks. In the multiclassification test, DL-IDS reached 98.67% in overall accuracy, and the accuracy of each attack type was above 99.50%.

Disruption of the blood-brain barrier (BBB) and the subsequent formation of brain edema is the most severe consequence of intracerebral hemorrhage (ICH), leading to drastic neuroinflammatory responses and neuronal cell death. A better understanding of ICH pathophysiology to develop effective therapy relies on selecting appropriate animal models. The collagenase injection ICH model and the autologous arterial whole blood infusion ICH model have been developed to investigate the pathophysiology of ICH. However, it remains unclear whether the temporal progression and the underlying mechanism of BBB breakdown are similar between these two ICH models. In this study, we aimed to determine the progression and the mechanism of BBB disruption via the two commonly used murine ICH models: the collagenase-induced ICH model (c-ICH) and the double autologous whole blood ICH model (b-ICH). Intrastriatal injection of 0.05 U collagenase or 20 μL autologous blood was used for a comparable hematoma volume in these two ICH models. Then we analyzed BBB permeability using Evan’s blue and IgG extravasation, evaluated tight junction (TJ) damage by transmission electron microscope (TEM) and Western blotting, and assessed matrix metalloproteinase-9 (MMP-9) activity and aquaporin 4 (AQP4) mRNA expression by Gelatin gel zymography and RT-PCR, respectively. The results showed that the BBB leakage was associated with a decrease in TJ protein expression and an increase in MMP-9 activity and AQP4 expression on day 3 in the c-ICH model compared with that on day 5 in the b-ICH model. Additionally, using TEM, we found that the TJ was markedly damaged on day 3 in the c-ICH model compared with that on day 5 in the b-ICH model. In conclusion, the BBB was disrupted in the two ICH models; compared to the b-ICH model, the c-ICH model presented with a more pronounced disruption of BBB at earlier time points, suggesting that the c-ICH model might be a more suitable model for studying early BBB damage and protection after ICH.

A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed. A key issue is that temporal information collected by neighbor detectors can make traffic missing data imputation more accurate. This review analyzes traffic temporal data imputation methods. Research methods, missing patterns, assumptions, imputation styles, application conditions, limitations, and public datasets are reviewed. Then, five representative methods are tested under different missing patterns and missing ratios. California performance measurement system (PeMS) data including traffic volume and speed are selected to conduct the test. Probabilistic principal component analysis performs the best under the most conditions.

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The Signal Phase and Timing (SPaT) message is an important input for research and applications of Connected Vehicles (CVs). However, the actuated signal controllers are not able to directly give the SPaT information since the SPaT is influenced by both signal control logic and real-time traffic demand. This study elaborates an estimation method which is proposed according to the idea that an actuated signal controller would provide similar signal timing for similar traffic states. Thus, the quantitative description of traffic states is important. The traffic flow at each approaching lane has been compared to fluids. The state of fluids can be indicated by state parameters, e.g. speed or height, and its energy, which includes kinetic energy and potential energy. Similar to the fluids, this paper has proposed an energy model for traffic flow, and it has also added the queue length as an additional state parameter. Based on that, the traffic state of intersections can be descripted. Then, a pattern recognition algorithm was developed to identify the most similar historical states and also their corresponding SPaTs, whose average is the estimated SPaT of this second. The result shows that the average error is 3.1 seconds.