Adversarial Danger Identification on Temporally Dynamic Graphs.

Multivariate time series forecasting plays an increasingly critical role in various applications, such as power management, smart cities, finance, and healthcare. Recent advances in temporal graph neural networks (GNNs) have shown promising results in multivariate time series forecasting due to their ability to characterize high-dimensional nonlinear correlations and temporal patterns. However, the vulnerability of deep neural networks (DNNs) constitutes serious concerns about using these models to make decisions in real-world applications.

Hierarchical MoP Hollow Nanospheres Anchored on a N,P,S-Doped Porous Carbon Matrix as Efficient Electrocatalysts for the Hydrogen Evolution Reaction.

Developing efficient, nonprecious, and durable electrocatalysts with favorable nanostructures is a persistent challenge yet is significant for the hydrogen evolution reaction (HER). Herein, for the first time, a rationally designed strategy is reported for the synthesis of hierarchical hollow MoP nanospheres anchored on N,P,S co-doped porous carbon (hs-MoP/NPSC). Importantly, the porous shell of the hollow nanosphere is constructed of a number of interwoven MoP subunits, which is beneficial for exposing surface active sites as much as possible and promoting the mass transport during the HER process.

Single-anastomosis duodeno-ileal bypass with sleeve gastrectomy (SADI-S) as a revisional surgery.

Background: Few studies of single-anastomosis duodeno-ileal bypass with sleeve gastrectomy (SADI-S) as the revision surgery for laparoscopic adjustable gastric banding (LAGB) have been published.

Objectives: To explore the efficacy and safety of SADI-S as the revision surgery for LAGB.

Setting: The research was completed by the University Hospital.