随着科技的飞速进展和教育方式的不断革新，传统的力学教学方法已无法满足现代社会的需求。新时代的力学教学应当与时俱进，创新方式和方法，使之更加适应现代社会的发展。首先，数字化与在线化是新时代教学的明显特征。通过利用虚拟现实（VR）、增强现实（AR）以及混合现实（MR）技术，我们可以为学生提供身临其境的学习体验，让抽象的力学知识变得直观和生动。此外，大数据和人工智能技术的应用，使得个性化学习成为可能。根据每个学生的学习进度和需求，为其提供量身定制的学习资源和方法。其次，注重跨学科的融合。现代的科学研究往往不再是单一学科的，而是多学科交叉的。新时代的力学教学应当强调与其他学科如物理、化学、生物和计算机科学的融合，让学生在学习力学的同时，也能培养跨学科的综合能力。再次，实践教学的重要性日益凸显。纯粹的理论教学往往难以激发学生的学习兴趣和动机。通过加强实验、设计和实践活动，不仅可以加深学生对力学知识的理解，也有助于培养他们的创新思维和实践能力。本报告将介绍新时代力学教学应当秉持的创新理念，以及新的教学方法和手段。

The concept of linear sets in projective spaces was introduced by Lunardon in 1999 and it plays central roles in the study of blocking sets, semifields, rank-metric codes and etc. A linear set with the largest possible cardinality is called scattered. Despite of two decades of study, there are only three known families of maximum scattered linear sets defined over PG(1,q^n) for infinitely many n. The first family is called pseudo-regulus type by Blokhuis and Lavrauw in 1999. The second family was found by Lunardon and Polverino in 2001 and later generalized by Sheekey in 2016. The third family was constructed by Longobardi, Marino, Trombetti and the speaker recently. In this talk, we consider the equivalence problem of maximum scattered linear sets and we show some restrictions on their automorphism groups.

Quadratically-constrained quadratic programming (QCQP) is an optimization problem that has many practical applications, for example, network localization problems, max-cut problems, and polynomial optimization problems. Since QCQP is NP-hard, semidefinite programming (SDP) relaxation is often employed to obtain a good approximate solution, and many researchers have discussed the condition for the SDP relaxation to give an exact optimal solution of the original QCQP.