Dual-pathway EEG model with channel attention for virtual reality motion sickness detection.

Background: Motion sickness has been a key factor affecting user experience in Virtual Reality (VR) and limiting the development of the VR industry. Accurate detection of Virtual Reality Motion Sickness (VRMS) is a prerequisite for solving the problem.

New Method: In this paper, a dual-pathway model with channel attention for detecting VRMS is proposed.

Kolmogorov Scaling in Turbulent 2D Bose-Einstein Condensates.

We investigated turbulence in 2D atomic Bose-Einstein condensates (BECs) using a minimally destructive, impurity injection technique analogous to particle image velocimetry in conventional fluids. Our approach transfers small regions of the BEC into a different hyperfine state and tracks their displacement, ultimately yielding the velocity field. This allows us to quantify turbulence in the same way as is conventional in fluid dynamics in terms of velocity-velocity correlation functions, called velocity structure functions, that obey Kolmogorov scaling laws.

FlowHON: Representing Flow Fields Using Higher-Order Networks.

Flow fields are often partitioned into data blocks for massively parallel computation and analysis based on blockwise relationships. However, most of the previous techniques only consider the first-order dependencies among blocks, which is insufficient in describing complex flow patterns. In this work, we present FlowHON, an approach to construct higher-order networks (HONs) from flow fields.

Circ-AARS plays an important role during the odontogenic differentiation of dental pulp stem cells by modulating miR-24-3p/KLF6 expression.

Background: Circular RNAs (circRNAs) play a crucial role in stem cell-based tooth regeneration. However, the functions and underlying mechanisms of circRNAs in tooth regeneration from human dental pulp stem cells (DPSCs) remain largely unclear.

Methods: In this study, DPSCs were used for odontogenic differentiation.

Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment.

Background: Endovascular treatment (EVT) has been recommended as a superior modality for the treatment of intracranial aneurysm. However, there still exists a worse percentage of poor functional outcome in patients with poor-grade aneurysmal subarachnoid hemorrhage (aSAH) undergoing EVT. Therefore, it is urgently needed to investigate the risk factors and develop a critical decision model in the subtype of such patients.