There are some limitations in the application of electrical muscle stimulation (EMS) in virtual reality (VR) that provides precise force feedback to the fingers. To address this issue, we propose EMS-Grasping, an interaction technique that combines biomechanical simulation model with EMS. This work makes two key contributions: (1) an EMS-based musculoskeletal model of the hand is constructed to establish the relationship between the electrical stimulation intensity and the angle of the finger joint; (2) a simulation model-based method for EMS control of finger fixation at a target angle is proposed and applied to grasping virtual objects in VR. In the first experiment, we verified the reliability of the simulation model. In the second experiment, we compared three interaction models to evaluate the performance of EMS-Grasping. The results show that EMS-Grasping not only effectively reduces finger penetration with virtual objects, but also enhances the user experience in terms of realism and comfort. This implies that EMS-Grasping has good potential in expressing grasping virtual objects.

In this study, we present a novel approach for 1-D magnetotelluric (MT) anisotropy inversion that aims to improve the reliability and efficiency of the inversion process. First, to reduce the number of inversion variables, we used the Fourier series (FS) expansion to approximate the resistivity distribution of the underground media. Consequently, the inversion variables are identified as the coefficients of the FS expansion, which are typically fewer in number than the layers. In addition, the boundary restriction of the recovered resistivity model is applied to further reduce the solution space and obtain a physically meaningful result. The objective function is formulated on the basis of the Tikhonov regularization method with an$L_{1}$norm, which facilitates accurate imaging for models with abrupt changes in resistivity. Then, we employ a new alternating direction method of multipliers (ADMM) algorithm to minimize the augmented Lagrangian function of the original objective function, thus deriving a set of resistivity parameters that can effectively illustrate the observation data. This process involves alternating updates of the inversion variables and two auxiliary variables during each iteration. Finally, our FS-based ADMM inversion method with the$L_{1}$norm, when subjected to extensive tests with synthetic and field datasets, has proved its superiority in terms of robustness, reliability, and efficiency in reconstructing subsurface resistivity distributions, compared to the conventional Gauss-Newton (GN) algorithm with the$L_{2}$norm. This offers the geoelectromagnetic community a novel and effective tool for the interpretation of MT data observed in anisotropic media.

In-memory key-value stores (IMKVSes) serve many online applications. They generally adopt the fork-based snapshot mechanism to support data backup. However, this method can result in query latency spikes because the engine is out-of-service for queries during the snapshot. In contrast to existing research optimizing snapshot algorithms, we address the problem from the operating system (OS) level, while keeping the data persistent mechanism in IMKVSes unchanged. Specifically, we first study the impact of the fork operation on query latency. Based on findings in the study, we propose Async-fork, which performs the fork operation asynchronously to reduce the out-of-service time of the engine. Async-fork is implemented in the Linux kernel and deployed into the online Redis database in public clouds. Our experiment results show that Async-fork can significantly reduce the tail latency of queries during the snapshot.