A study of the transient gas flow affected ion transmission in atmospheric pressure interfaces based on large eddy simulation for electrospray ionization mass spectrometry.

Modern atmosphere pressure interface (API) enables high-efficiency coupling between mass analyzers in high vacuum and atmosphere ionization sources such as electrospray ionization (ESI) source. The transient gas flow entering API possesses strong compressibility and turbulent characteristics, which exerts a huge impact on ion transmission. However, the instantaneous nature and vortical morphology of the turbulence in API and its affection in ion transmission were hardly covered in the reported research.

The High-Efficiency Calculation and Analysis of Different Quadrupole Fields Based on the Method of Fundamental Solution.

The calculation and analysis of electric fields are indispensable steps in the design of mass spectrometry. In this work, an approach for this calculation was established based on the method of fundamental solution (MFS). It was proved to be much faster and more accurate than the other popular methods, and its optimum parameters were found for the calculation of different quadrupole fields.

Exploring the causes of augmentation in restless legs syndrome.

Long-term drug treatment for Restless Legs Syndrome (RLS) patients can frequently result in augmentation, which is the deterioration of symptoms with an increased drug dose. The cause of augmentation, especially derived from dopamine therapy, remains elusive. Here, we review recent research and clinical progress on the possible mechanism underlying RLS augmentation.

From droplets to ions: a comprehensive and consecutive ion formation modelling in atmosphere pressure interface of electrospray ionization mass spectrometry.

In this study, we propose a novel ion formation simulation method for electrospray ionization (ESI) and atmosphere pressure interface (API). In this method, not the sheer particle trajectory, but the evolution of droplets and the offspring of gaseous ions are introduced instead. For the first time, the dynamic droplet-to-ion transformation process in the API of ESI-MS is visualized.

Event-Triggered Multiasynchronous H Control for Markov Jump Systems With Transmission Delay.

In this article, the issue of event-triggered multiasynchronous H control for Markov jump systems with transmission delay is concerned. In order to reduce sampling frequency, multiple event-triggered schemes (ETSs) are introduced. Then hidden Markov model (HMM) is employed to describe multiasynchronous jumps among subsystems, ETSs, and controller.

Adaptive Neural State Estimation of Markov Jump Systems Under Scheduling Protocols and Probabilistic Deception Attacks.

The neural-network (NN)-based state estimation issue of Markov jump systems (MJSs) subject to communication protocols and deception attacks is addressed in this article. For relieving communication burden and preventing possible data collisions, two types of scheduling protocols, namely: 1) the Round-Robin (RR) protocol and 2) weighted try-once-discard (WTOD) protocol, are applied, respectively, to coordinate the transmission sequence. In addition, considering that the communication channel may suffer from mode-dependent probabilistic deception attacks, a hidden Markov-like model is proposed to characterize the relationship between the malicious signal and system mode.

Event-Triggered and Self-Triggered L Control for Markov Jump Stochastic Nonlinear Systems Under DoS Attacks.

This article investigates event-triggered and self-triggered L control problems for the Markov jump stochastic nonlinear systems subject to denial-of-service (DoS) attacks. When attacks prevent system devices from obtaining valid information over networks, a new switched model with unstable subsystems is constructed to characterize the effect of DoS attacks. On the basis of the switched model, a multiple Lyapunov function method is utilized and a set of sufficient conditions incorporating the event-triggering scheme (ETS) and restriction of DoS attacks are provided to preserve L performance.

Adaptive Neural Event-Triggered Control of Networked Markov Jump Systems Under Hybrid Cyberattacks.

This article is concerned with the neural network (NN)-based event-triggered control problem for discrete-time networked Markov jump systems with hybrid cyberattacks and unmeasured states. The event-triggered mechanism (ETM) is used to reduce the communication load, and a Luenberger observer is introduced to estimate the unmeasured states. Two kinds of cyberattacks, denial-of-service (DoS) attacks and deception attacks, are investigated due to the vulnerability of cyberlayer.

Event-Triggered Resilient L Control for Markov Jump Systems Subject to Denial-of-Service Jamming Attacks.

In this article, the event-triggered resilient L control problem is concerned for the Markov jump systems in the presence of denial-of-service (DoS) jamming attacks. First, a fixed lower bound-based event-triggering scheme (ETS) is presented in order to avoid the Zeno problem caused by exogenous disturbance. Second, when DoS jamming attacks are involved, the transmitted data are blocked and the old control input is kept by using the zero-order holder (ZOH).

Glutamatergic and gabaergic neuronal populations in the dorsal Periacqueductual Gray have different functional roles in aversive conditioning.

The dorsal periacqueductal gray (dPAG) is a midbrain structure having an essential role in coordinating defensive behaviors in response to aversive stimulation. However, the question of whether dPAG neurons can respond to aversive conditioning and retrieval, properties involved in emergence of negative emotional state, is still under debate. Here we used calcium imaging by fiber photometry to record the activity of dPAG and dPAG neuronal populations during unconditioned and conditioned aversive stimulation.

Characterization of glutamatergic VTA neural population responses to aversive and rewarding conditioning in freely-moving mice.

The Ventral Tegmental Area (VTA) is a midbrain structure known to integrate aversive and rewarding stimuli, but little is known about the role of VTA glutamatergic (VGluT2) neurons in these functions. Direct activation of VGluT2 soma evokes rewarding behaviors, while activation of their downstream projections evokes aversive behaviors. To facilitate our understanding of these conflicting properties, we recorded calcium signals from VTA neurons using fiber photometry in VGluT2-cre mice to investigate how this population was recruited by aversive and rewarding stimulation, both during unconditioned and conditioned protocols.