A UK-led research collaboration has taken a significant step in the global effort to protect cereal crops from the devastating blast fungus pathogen.

A joint team of researchers at Bar-Ilan University and the Weizmann Institute has used advanced computational design methods to enhance the ability of genetically engineered immune cells to recognize and attack cancer and viral targets.

With heavy reusable launch vehicles becoming a key technology for low-cost and high-frequency space exploration, cable-net recovery schemes have attracted considerable attention due to their strong adaptability to landing dispersion and attitude deviations. However, under multiple sources of interference—including engine plume impingement, trolley motion, and flexible vibrations—the dynamic state information of the flexible cable-net recovery system is difficult to acquire directly, severely constraining the accuracy of closed-loop control and capture reliability. Although Kalman filtering combined with multibody dynamics models can achieve state estimation for complex systems, the flexible cable-net system is inherently a high-dimensional, nonlinear, strongly rigid-flexible coupled constrained multibody system, whose dynamic equations are formulated as index-3 differential-algebraic equations (DAEs). Conventional methods require the conversion of DAEs into ordinary differential equations (ODEs) followed by linearization, a process that is complex and lacks generality; conventional numerical integration methods converge slowly when dealing with such stiff problems, with the Jacobian matrix becoming severely ill-conditioned under large time steps and constraints being prone to violation. Therefore, developing a filtering method that does not require model transformation, can directly utilize the multibody DAE for state prediction, and can maintain constraint satisfaction has become a key technical challenge for state estimation in flexible cable-net recovery.

Large-scale studies across the Nature journals create one of the world’s most comprehensive single-cell atlases of Alzheimer’s disease and related neuropsychiatric conditions