Bulk metallic glasses of ${\mathrm{Nd}}_{\mathrm{65}}{\mathrm{Al}}_{\mathrm{10}}{\mathrm{Fe}}_{\text{25−x}}{\mathrm{Co}}_x$ $\text{(x=0,5,10)}$ have been prepared in the form of 3 mm diam rods. Results of differential scanning calrimetry, dynamic mechanical thermal analysis (DMTA), and x-ray diffraction are presented for these alloys. It is shown that the glass transition and crystallization have been observed by DMTA. The reduced glass transition temperature of these glasses, defined as the ratio between the glass transition temperature $T_g$ and the melting temperature $T_l$ is in the range from 0.55 to 0.62. All these glasses have a large supercooled liquid region (SLR), ranging from 80 to 130 K. The high value of reduced glass transition temperature and wide SLR agree with their good glass formation ability.

The primary objective of this study was to use the cellular automata (CA) method to model the characteristics of bicycle passing events in mixed bicycle traffic on separated bicycle paths. The mixed traffic was composed of two types of bicycles: the conventional bicycle (c-bicycle) and the electric bicycle (e-bicycle). The number of passing events and the characteristics of mixed bicycle traffic were investigated in the field on eight physically separated bicycle paths in China. Then a CA model was calibrated with the use of the field data to simulate the passing events in the mixed bicycle traffic. The results showed that the CA model could simulate the features of bicycle passing events well. The simulation results were consistent with field observations. An increase in the ratio of e-bicycles would not significantly increase the number of passing events, but e-bicycles did contribute substantially to passing events in mixed bicycle traffic. E-bicycles were shown to have more stability than c-bicycles, especially in traffic with a high flow rate. The findings of this study could improve the performance of simulation techniques to reflect the actual characteristics of mixed bicycle traffic.

Multiple spin-glass-like (SGL) behaviors have been observed in a ${\mathrm{Pr}}_{60}{\mathrm{Al}}_{10}{\mathrm{Ni}}_{10}{\mathrm{Cu}}_{16}{\mathrm{Fe}}_4$ bulk metallic glass (BMG). Various measurements are carried out to confirm the coexistence of a reentrant-SGL ordering at $14\mathrm{K}$, and two cluster-SGL transitions around $6\mathrm{K}$ and $280\mathrm{K}$. The unusual multiple SGL behaviors are ascribed to the couple between the magnetic nanoclusters and the amorphous matrix. The combination of the intrinsic inhomogeneity, spontaneous nanoconfinement and magnetic completing in the BMG leads to the three qualitatively distinct SGL transitions with unique characteristics such as the elevated freezing temperature close to room temperature in cluster SGL. Comparing with the most spin glass (SG) materials with only one SG phase, the Pr-based BMG with multiple SGL phases provides an ideal prototype to fill up the gap for the unknown interactions between two or more SGs, which is of significance to expand current understanding of both SG and more complex systems.

Pavement cracks can result in the degradation of pavement performance. Due to the lack of timely inspection and reparation for the pavement cracks, with the development of cracks, the safety and service life of the pavement can be decreased. To curb the development of pavement cracks, detecting these cracks accurately plays an important role. In this paper, an automatic pavement crack detection method is proposed. For achieving real-time inspection, the YOLOV5 was selected as the base model. Due to the small size of the pavement cracks, the accuracy of most of the pavement crack deep learning-based methods cannot reach a high degree. To further improve the accuracy of those kind of methods, attention modules were employed. Based on the self-building datasets collected in Linyi city, the performance among various crack detection models was evaluated. The results showed that adding attention modules can effectively enhance the ability of crack detection. The precision of YOLOV5-CoordAtt reaches 95.27%. It was higher than other conventional and deep learning methods. According to the pictures of the results, the proposed methods can detect accurately under various situations.