Researchers from Donghua University (Shanghai, China) have developed a novel heterogeneous catalyst, diimine-Ni@PCN-701, by immobilizing diimine-nickel complexes into a zirconium-based metal-organic framework (MOF). This catalyst exhibits exceptional activity for propylene dimerization, achieving a turnover frequency (TOF) of up to 56900 h⁻¹ under mild conditions. It selectively converts propylene into C6 dimers, predominantly 4-methyl-2-pentene (4M2P), with 100% selectivity toward dimers. The enhanced performance arises from the electron-withdrawing effect of the Zr₆O₄(OH)₄ clusters and the steric confinement of the MOF pores, offering a general strategy for designing high-performance heterogeneous olefin oligomerization catalysts.

Researchers from Donghua University (Shanghai, China) have made a theoretical development in relational database normalization by investigating the computational complexity of prime attributes and keys specifically for relation schemes in second normal form (2NF). The study demonstrates that even when a relation scheme is known to be in 2NF, the prime attribute problem remains NP-complete, and the number of all keys can grow exponentially with the number of attributes and functional dependencies. The team also proposes a novel algorithm to find all keys in a 2NF relation scheme, deepening the theoretical understanding of 2NF normalization and providing practical references for database schema design.

Traditional server-based datacenters suffer from low resource utilization, insufficient hardware scalability, insufficient resource usage elasticity, and low fault tolerance. With the ever-increasing network speed and processing power in hardware controllers, physically disaggregating the coupled resources in a server has been a promising trend for building next-generation Datacenters, as it effectively addresses the limitations of server-based datacenters.

Non line of sight (NLOS) imaging achieves imaging of occluded objects by analyzing the reflection and scattering of relay surfaces. Deep learning methods are mainly used to restore two-dimensional hidden scenes and face challenges in reconstructing complex real-world scenes. To solve the problems, a research team led by Hao Liu and Mingliang Xu published their new study in Frontiers of Computer Science on 15 May 2026.

Existing research has focused on trajectory similarity computation under centralized data storage. However, in the real world, trajectories are often collected and held by multiple distinct entities that form a data federation. Due to commercial and legal restrictions, direct data sharing among these entities may be prohibited, which renders existing centralized methods incapable of supporting similarity computation over trajectory data federation.

Researchers from the Tata Institute of Fundamental Research Hyderabad (TIFRH), Roni Chatterjee and Monoj Adhikari from the group of Prof. Smarajit Karmakar, have uncovered how the intrinsic fragility of glassy materials determines whether they fail in a brittle or ductile manner. Their findings, published in Nature Communications, provide new insight into the mechanical behavior of glasses.

Glassy materials, including window glass, metallic glasses, polymers, foams, and gels, can either deform smoothly under stress (ductility) or fracture suddenly (brittleness). While fragility has traditionally been used to describe how rapidly a liquid slows down as it approaches the glassy state during cooling, this study demonstrates that it also governs how the resulting glass responds to mechanical loading.

Using large-scale computer simulations, the researchers prepared different glassy materials over a wide range of temperatures and subjected them to repeated cyclic deformation. By analyzing stress-strain curves, they measured the yield strain—the maximum deformation a material can withstand before failure—and the associated stress drop, which indicates the degree of brittleness.

The study reveals that in fragile glasses, energy barriers grow rapidly during cooling. Consequently, larger strain is required to initiate yielding, leading to higher yield strain, larger stress accumulation, and a sharp stress drop characteristic of brittle failure. In contrast, strong glasses exhibit a gradual increase in energy barriers with cooling, resulting in only a slight change in yield strain and a much smaller stress drop, indicative of ductile behavior.

By linking mechanical yielding directly to the evolution of energy barriers, the work establishes fragility as a fundamental factor controlling whether a glass breaks abruptly or deforms gradually. These findings provide a deeper understanding of the mechanical properties of amorphous materials and could guide the design of safer and more reliable glasses for structural, industrial, and technological applications.