Coseismic Coulomb stress changes induced by a 2020-2021 M_(W)>7.0 Alaska earthquake sequence in and around the Shumagin gap and its influence on the Alaska-Aleutian subduction interface

Three M_(W)>7.0 earthquakes in 2020-2021 occurred in the Shumagin seismic gap and its adjacent area of the Alaska-Aleutian subduction zone,including the Mw7.8 Simeonof thrust earthquake on July 22,2020,the M_(W)7.6 Sand Point strike-slip earthquake on October 19,2020,and the M_(W)8.2 Chignik thrust earthquake on July 29,2021.The spatial and temporal proximity of these three earthquakes prompts us to probe stress-triggering effects among them.Here we examine the coseismic Coulomb stress change imparted by the three earthquakes and their influence on the subduction interface.Our results show that:(1)The Simeonof earthquake has strong loading effects on the subsequent Sand Point and Chignik earthquakes,with the Coulomb stress changes of 3.95 bars and 2.89 bars,respectively.The Coulomb stress change caused by the Sand Point earthquake at the hypocenter of the Chignik earthquake is merely around 0.01 bars,suggesting the negligible triggering effect on the latter earthquake;(2)The triggering effects of the Simeonof,Sand Point,and Chignik earthquakes on aftershocks within three months are not well pronounced because of the triggering rates of 38%,14%,and 43%respectively.Other factors may have played an important role in promoting the occurrence of these aftershocks,such as the roughness of the subduction interface,the complicated velocity structure of the lithosphere,and the heterogeneous prestress therein;(3)The three earthquakes caused remarkable coseismic Coulomb stress changes at the subduction interface nearby these mainshocks,with an average Coulomb stress change of 3.2 bars in the shallow region directly inwards the trench.

Common Spatial Pattern Ensemble Classifier and Its Application in Brain-Computer Interface

Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble （CSPE） classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.

Real-Time Detection of Human Drowsiness via a Portable Brain-Computer Interface

In this paper, we proposed a new concept: depth of drowsiness, which can more precisely describe the drowsiness than existing binary description. A set of effective markers for drowsiness: normalized band norm was successfully developed. These markers are invariant from voltage amplitude of brain waves, eliminating the need for calibrating the voltage output of the brain-computer interface devices. A new polling algorithm was designed and implemented for computing the depth of drowsiness. The time cost of data acquisition and processing for each estimate is about one second, which is well suited for real-time applications. Test results with a portable brain-computer interface device show that the depth of drowsiness computed by the method in this paper is generally invariant from ages of test subjects and sensor channels (P3 and C4). The comparison between experiment and computing results indicate that the new method is noticeably better than one of the recent methods in terms of accuracy for predicting the drowsiness.

Real-Time Brain-Computer Interface System Based on Motor Imagery

Abstract-A brain-computer interface （BCI） real- time system based on motor imagery translates the user＇s motor intention into a real-time control signal for peripheral equipments. A key problem to be solved for practical applications is real-time data collection and processing. In this paper, a real-time BCI system is implemented on computer with electroencephalogram amplifier. In our implementation, the on-line voting method is adopted for feedback control strategy, and the voting results are used to control the cursor horizontal movement. Three subjects take part in the experiment. The results indicate that the best accuracy is 90%.

Neurological rehabilitation of stroke patients via motor imaginary-based brain-computer interface technology

The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classification were significantly greater at 4 weeks after treatment （P 〈 0.01）, which suggested that motor imaginary-based brain-computer interface technology improved balance and walking in stroke patients.

Probabilistic Methods in Multi-Class Brain-Computer Interface

Abstract-Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface （BCI）： support vector machine （SVM） with posteriori probability （PSVM） and Bayesian linear discriminant analysis with probabilistic output （PBLDA）. A comparative evaluation of these two methods is conducted. The results shows that： 1） probabilistie information can improve the performance of BCI for subjects with high kappa coefficient, and 2） PSVM usually results in a stable kappa coefficient whereas PBLDA is more efficient in estimating the model parameters.

Possibility to Realize the Brain-Computer Interface from the Quantum Brain Model Based on Superluminal Particles

R. Penrose and S. Hameroff have proposed an idea that the brain can attain high efficient quantum computation by functioning of microtubular structure of neurons in the cytoskelton of biological cells, including neurons of the brain. But Tegmark estimated the duration of coherence of a quantum state in a warm wet brain to be on the order of 10>–13

Performance and Implementations of Vibrotactile Brain-Computer Interface with Ipsilateral and Bilateral Stimuli

The tactile P300 brain-computer interface( BCI) is related to the somatosensory perception and response of the human brain,and is different from visual or audio BCIs. Recently,several studies focused on the tactile stimuli delivered to different parts of the human body. Most of these stimuli were symmetrically bilateral.Only a fewstudies explored the influence of tactile stimuli laterality.In the current study,we extensively tested the performance of a vibrotactile BCI system using ipsilateral stimuli and bilateral stimuli.Two vibrotactile P300-based paradigms were tested. The target stimuli were located on the left and right forearms for the left forearm and right forearm( LFRF) paradigm,and on the left forearm and calf for the left forearm and left calf( LFLC)paradigm. Ten healthy subjects participated in this study. Our experiments and analysis showed that the bilateral paradigm( LFRF) elicited larger P300 amplitude and achieved significantly higher classification accuracy than the ipsilateral paradigm( LFLC). However, both paradigms achieved classification accuracies higher than 70% after the completion of several trials on average,which was usually regarded as the minimum accuracy level required for BCI system to be deemed useful.

Design of an EEG Preamplifier for Brain-Computer Interface

As a non-invasive neurophysiologieal index for brain-computer interface （BCI）, electroencephalogram （EEG） attracts much attention at present. In order to have a portable BCI, a simple and efficient pre-amplifier is crucial in practice. In this work, a preamplifier based on the characteristics of EEG signals is designed, which consists of a highly symmetrical input stage, low-pass filter, 50 Hz notch filter and a post amplifier. A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment. The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.

The miR-9-5p/CXCL11 pathway is a key target of hydrogen sulfide-mediated inhibition of neuroinflammation in hypoxic ischemic brain injury

We previously showed that hydrogen sulfide(H2S)has a neuroprotective effect in the context of hypoxic ischemic brain injury in neonatal mice.However,the precise mechanism underlying the role of H2S in this situation remains unclear.In this study,we used a neonatal mouse model of hypoxic ischemic brain injury and a lipopolysaccharide-stimulated BV2 cell model and found that treatment with L-cysteine,a H2S precursor,attenuated the cerebral infarction and cerebral atrophy induced by hypoxia and ischemia and increased the expression of miR-9-5p and cystathionineβsynthase(a major H2S synthetase in the brain)in the prefrontal cortex.We also found that an miR-9-5p inhibitor blocked the expression of cystathionineβsynthase in the prefrontal cortex in mice with brain injury caused by hypoxia and ischemia.Furthermore,miR-9-5p overexpression increased cystathionine-β-synthase and H2S expression in the injured prefrontal cortex of mice with hypoxic ischemic brain injury.L-cysteine decreased the expression of CXCL11,an miR-9-5p target gene,in the prefrontal cortex of the mouse model and in lipopolysaccharide-stimulated BV-2 cells and increased the levels of proinflammatory cytokines BNIP3,FSTL1,SOCS2 and SOCS5,while treatment with an miR-9-5p inhibitor reversed these changes.These findings suggest that H2S can reduce neuroinflammation in a neonatal mouse model of hypoxic ischemic brain injury through regulating the miR-9-5p/CXCL11 axis and restoringβ-synthase expression,thereby playing a role in reducing neuroinflammation in hypoxic ischemic brain injury.

The diffusive fluxes of inorganic nitrogen across the intertidal sediment-water interface of the Changjiang Estuary in China

Ammonium and nitrate concentrations were analyzed in near-bottom water and pore water collected from ten stations of the intertidal flat of the Changjiang Estuary during April, July, November and February. The magnitudes of the benthic exchange fluxes were determined on the basis of concentration gradients of ammonium and nitrate at the near-bottom water and interstitial water interface in combination with calculations of a modified Fick＇ s first law. Ammonium fluxes varied from - 5.05 to 1.43 μg/（ cm^2·d） and were greatly regulated by the production of ammonium in surface sediments, while nitrate fluxes ranged from - 0. 38 to 1.36 μg/ （ cm^2·d） and were dominated by nitrate concentrations in the tidal water. It was found that ammonium was mainly released from sediments into water columns at most of stations whereas nitrate was mostly diffused from overlying waters to intertidal sediments. In total, 823.75 t/a ammonium-N was passed from intertidal sediments to water while about 521.90 t/a nitrate-N was removed from overlying waters to intertidal sediments. This suggests that intertidal sediments had the significant influence on modulating inorganic nitrogen in the tidal water.

EEG classification based on probabilistic neural network with supervised learning in brain computer interface

Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.

Exosomes derived from microglia overexpressing miR-124-3p alleviate neuronal endoplasmic reticulum stress damage after repetitive mild traumatic brain injury

We previously reported that miR-124-3p is markedly upregulated in microglia-derived exosomes following repetitive mild traumatic brain injury.However,its impact on neuronal endoplasmic reticulum stress following repetitive mild traumatic brain injury remains unclear.In this study,we first used an HT22 scratch injury model to mimic traumatic brain injury,then co-cultured the HT22 cells with BV2 microglia expressing high levels of miR-124-3p.We found that exosomes containing high levels of miR-124-3p attenuated apoptosis and endoplasmic reticulum stress.Furthermore,luciferase reporter assay analysis confirmed that miR-124-3p bound specifically to the endoplasmic reticulum stress-related protein IRE1α,while an IRE1αfunctional salvage experiment confirmed that miR-124-3p targeted IRE1αand reduced its expression,thereby inhibiting endoplasmic reticulum stress in injured neurons.Finally,we delivered microglia-derived exosomes containing miR-124-3p intranasally to a mouse model of repetitive mild traumatic brain injury and found that endoplasmic reticulum stress and apoptosis levels in hippocampal neurons were significantly reduced.These findings suggest that,after repetitive mild traumatic brain injury,miR-124-3 can be transferred from microglia-derived exosomes to injured neurons,where it exerts a neuroprotective effect by inhibiting endoplasmic reticulum stress.Therefore,microglia-derived exosomes containing miR-124-3p may represent a novel therapeutic strategy for repetitive mild traumatic brain injury.

Adaptive neuro-fuzzy interface system for gap acceptance behavior of right-turning vehicles at partially controlled T-intersections

Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed coun tries. Intersections with no specific priority to any move ment, known as uncontrolled intersections, are common in India. Limited priority is observed at a few intersections, where priorities are perceived by drivers based on geom etry, traffic volume, and speed on the approaches of intersection. Analyzing such intersections is complex because the overall traffic behavior is the result of drivers, vehicles, and traffic flow characteristics. Fuzzy theory has been widely used to analyze similar situations. This paper describes the application of adaptive neurofuzzy interface system （ANFIS） to the modeling of gap acceptance behavior of rightturning vehicles at limited priority Tintersections （in India, vehicles are driven on the left side of a road）. Field data are collected using video cameras at four Tintersections having limited priority. The data extracted include gap/lag, subject vehicle type, conflicting vehicle type, and driver＇s decision （accepted/rejected）. ANFIS models are developed by using 80 % of the extracted data （total data observations for major road right turning vehicles are 722 and 1,066 for minor road right turning vehicles） and remaining are used for model vali dation. Four different combinations of input variables are considered for major and minor road right turnings sepa rately. Correct prediction by ANFIS models ranges from 75.17 % to 82.16 % for major road right turning and 87.20 % to 88.62 % for minor road right turning. Themodels developed in this paper can be used in the dynamic estimation of gap acceptance in traffic simulation models.

33% Classification Accuracy Improvement in a Motor Imagery Brain Computer Interface

A right-hand motor imagery based brain-computer interface is proposed in this work. Such a system requires the identification of different brain states and their classification. Brain signals recorded by electroencephalography are naturally contaminated by various noises and interferences. Ocular artifact removal is performed by implementing an auto-matic method “Kmeans-ICA” which does not require a reference channel. This method starts by decomposing EEG signals into Independent Components;artefactual ones are then identified using Kmeans clustering, a non-supervised machine learning technique. After signal preprocessing, a Brain computer interface system is implemented;physiologically interpretable features extracting the wavelet-coherence, the wavelet-phase locking value and band power are computed and introduced into a statistical test to check for a significant difference between relaxed and motor imagery states. Features which pass the test are conserved and used for classification. Leave One Out Cross Validation is performed to evaluate the performance of the classifier. Two types of classifiers are compared: a Linear Discriminant Analysis and a Support Vector Machine. Using a Linear Discriminant Analysis, classification accuracy improved from 66% to 88.10% after ocular artifacts removal using Kmeans-ICA. The proposed methodology outperformed state of art feature extraction methods, namely, the mu rhythm band power.

An efficient approach of EEG feature extraction and classification for brain computer interface

In the study of brain-computer interfaces,a method of feature extraction and classification used fortwo kinds of imaginations is proposed.It considers Euclidean distance between mean traces recorded fromthe channels with two kinds of imaginations as a feature,and determines imagination classes using thresh-old value.It analyzed the background of experiment and theoretical foundation referring to the data sets ofBCI 2003,and compared the classification precision with the best result of the competition.The resultshows that the method has a high precision and is advantageous for being applied to practical systems.

On Similarities and Differences of Invasive and Non-Invasive Electrical Brain Signals in Brain-Computer Interfacing

We perceive that some Brain-Computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although invasive and non-invasive BCI signals are different, the underlying origin of electrical BCIs signals is the same.

Dislocation stability in three-phase nanocomposites with imperfect interface

Interface imperfection can significantly affect the mechanical properties and failure mechanisms as well as the strength and toughness of nanocomposites. The elastic behavior of a screw dislocation in nanoscale coating with imperfect interface is studied in the three-phase composite cylinder model. The interface between inner nanoin- homogeneity and intermediate coating is assumed as perfectly bonded. The bonding between intermediate coating and outer matrix is considered to be imperfect with the assumption that interface imperfection is uniform, and a linear spring model is adopted to describe the weakness of imperfect interface. The explicit expression for image force acting on dislocation is obtained by means of a complex variable method. The analytic results indicate that inner interface effect and outer interface imperfection, simultaneously taken into account, would influence greatly image force, equilibrium position and stability of dislocation, and various critical parameters that would change dislocation stability. The weaker interface is a very strong trap for glide dislocation and, thus, a more effective barrier for slip transmission.

Materials Design on the Origin of Gap States in a High-κ/GaAs Interface

Given the demand for constantly scaling micro- electronic devices to ever smaller dimensions, a SiO2 gate dielectric was substituted with a higher dielectric-constant material, Hf（Zr）O2, in order to minimize current leakage through dielectric thin film. However, upon interfacing with high dielectric constant （high-κ） dielectrics, the electron mobility in the conventional Si channel degrades due to Coulomb scattering, surface-roughness scattering, remotephonon scattering, and dielectric-charge trapping.Ⅲ-Ⅴ and Ge are two promising candidates with superior mobility over Si. Nevertheless, Hf（Zr）O2/Ⅲ-Ⅴ（Ge） has much more complicated interface bonding than Si-based interfaces. Successful fabrication of a high-quality device critically depends on understanding and engineering the bonding configurations at Hf（Zr）O2/Ⅲ-Ⅴ（Ge） interfaces for the optimal design of device interfaces. Thus, an accurate atomic insight into the interface bonding and mechanism of interface gap states formation becomes essential. Here, we utilize first- principle calculations to investigate the interface between HfO2 and GaAs. Our study shows that As--As dimer bonding, Ga partial oxidation （between 3＋ and 1＋） and Ga- dangling bonds constitute the major contributions to gap states. These findings provide insightful guidance for optimum interface passivation.