Improvement of mechanical properties and investigation of strengthening mechanisms on the Ti_(3)AlC_(2) ceramic with nanosized WC addition

Ti_(3)AlC_(2) (TAC) has great potential for use as an ablation material in aerospace applications due to its great oxidation/ablation resistance, but its high-temperature strength and thermal shock resistance still have much room for simultaneous improvement under fast temperature variation conditions. Herein, we used Ti_(3)AlC_(2) and WC powders as raw materials and successfully fabricated textured (Ti,W)_(3)AlC_(2) ceramic with small amounts of TiC and Al_(2)O_(3), and room temperature mechanical properties such as flexural strength (1146±46.9 MPa), fracture toughness (11.78±0.44 MPa·m^(1/2)), and hardness (5.81±0.11 GPa) at 5 wt% WC addition were achieved. The high-temperature strength of the ceramic was significantly improved, and better thermal shock resistance from 298 to 1173 K was simultaneously acquired together with the regulation of the elastic modulus, thermal conductivity, and thermal expansion coefficient, providing (Ti,W)_(3)AlC_(2) with more possibilities for fast-temperature variation applications. Strengthening and toughening mechanisms were proposed. Scanning transmission electron microscopy high-angle annular dark-field imaging (STEM-HADDF) showed that W randomly replaced the Ti1 and Ti2 sites of Ti_(3)AlC_(2), providing a good reference for establishing crystal models, and further density functional theory (DFT) calculations based on these models indicated a higher fracture energy of (Ti,W)_(3)AlC_(2) along different crystal planes, providing superior resistance to transgranular fracture;a lower mismatch degree of (Ti,W)_(3)AlC_(2)/Al_(2)O_(3) resulted in stronger interface bonding, resulting in greater resistance to intergranular fracture as well as more balanced stress distributions at different interfaces.

PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data

Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time,consequently helping them avoid congestion and accidents to a certain extent.However,the existing methods for real-time urban traffic flow prediction focus on improving the model prediction accuracy or efficiency while ignoring the training efficiency,which results in a prediction system that lacks the scalability to integrate real-time traffic flow into the training procedure.To conduct accurate and real-time urban traffic flow prediction while considering the latest historical data and avoiding time-consuming online retraining,herein,we propose a scalable system for Predicting short-term URban traffic flow in real-time based on license Plate recognition data(PURP).First,to ensure prediction accuracy,PURP constructs the spatio-temporal contexts of traffic flow prediction from License Plate Recognition(LPR)data as effective characteristics.Subsequently,to utilize the recent data without retraining the model online,PURP uses the nonparametric method k-Nearest Neighbor(namely KNN)as the prediction framework because the KNN can efficiently identify the top-k most similar spatio-temporal contexts and make predictions based on these contexts without time-consuming model retraining online.The experimental results show that PURP retains strong prediction efficiency as the prediction period increases.