Influence of casting temperature on microstructures and mechanical properties of Cu50Zr45.5Ti2.5Y2 metallic glass prepared using copper mold casting [+ Erratum]

We investigated the influence of casting temperatures on microstructures and mechanical properties of rapidly solidified Cu50Zr45.5Ti2.5Y2 alloy. With casting temperatures increasing, the content of the crystalline phase decreases. At high casting temperature, i.e., 1723 K, glass forming ability (GFA) of the present alloy enhanced. It is implied that adjusting casting temperatures could be used for designing the microstructures of bulk metallic glass matrix composite (BMGC). Nano-indentation tests indicated that CuZr phases is a little softer and can accommodate more plastic deformation than the amorphous matrix. Compression tests confirmed that this kind of the second phase (CuZr) precipitated under lower casting temperatures helps to initiate multiple shear bands, resulting in great improvement of mechanical properties of the samples. Our work indicate that casting temperatures lead a great influence on GFA, microstructures and mechanical properties of rapidly solidified alloy and controlling casting temperatures is crucial to the application of BMGs

Application of Smart Mobile Devices in Electronic Design Education: Multidimensional Interaction Model and Learning Outcomes Assessment

With the rapid advancement of technology, smart mobile devices are increasingly being integrated into the educational domain, showing significant potential, particularly in electronic design education. Traditional classroom teaching methods face limitations in information delivery and student interaction, while the introduction of smart mobile devices brings new opportunities to classroom instruction. Through smart mobile devices, educators can organize teaching activities more flexibly, and students can engage in classroom interactions in various forms, greatly enhancing teaching effectiveness and learning experiences. Although numerous studies have explored the application of smart mobile devices in education, most focus on single-dimensional interaction models, overlooking the potential of multidimensional interactions. Additionally, traditional methods for assessing learning outcomes often rely on qualitative analysis and post-class tests, which fail to comprehensively and in realtime reflect students’ learning states and emotional changes. This paper aims to construct a multidimensional interaction model for electronic design education classrooms based on smart mobile devices and to assess learning outcomes through real-time analysis of students’ emotions and feedback data, thereby optimizing teaching strategies. This study not only provides new perspectives and methods for the application of smart mobile devices in education but also offers practical guidance for teaching reforms and innovations in electronic design education, holding significant theoretical and practical value

GCC: Generative Calibration Clustering

Deep clustering as an important branch of unsupervised representation learning focuses on embedding semantically similar samples into the identical feature space. This core demand inspires the exploration of contrastive learning and subspace clustering. However, these solutions always rely on the basic assumption that there are sufficient and category-balanced samples for generating valid high-level representation. This hypothesis actually is too strict to be satisfied for real-world applications. To overcome such a challenge, the natural strategy is utilizing generative models to augment considerable instances. How to use these novel samples to effectively fulfill clustering performance improvement is still difficult and under-explored. In this paper, we propose a novel Generative Calibration Clustering (GCC) method to delicately incorporate feature learning and augmentation into clustering procedure. First, we develop a discriminative feature alignment mechanism to discover intrinsic relationship across real and generated samples. Second, we design a self-supervised metric learning to generate more reliable cluster assignment to boost the conditional diffusion generation. Extensive experimental results on three benchmarks validate the effectiveness and advantage of our proposed method over the state-of-the-art methods