NiCo alloy-decorated nitrogen-doped carbon double-shelled hollow polyhedrons with abundant catalytic active sites to accelerate lithium polysulfides conversion.

Lithium-sulfur (Li-S) batteries have received significant attention due to their high theoretical energy density. However, the inherent poor conductivity of S and lithium sulfide (LiS), coupled with the detrimental shuttle effect induced by lithium polysulfides (LiPSs), impedes their commercialization. In this study, we develop NiCo alloy-decorated nitrogen-doped carbon double-shelled hollow polyhedrons (NC/NiCo DSHPs) as highly efficient catalysts for Li-S batteries.

The CoFeNC@NC Catalyst with Numerous Surface Cracks Bidirectionally Catalyzes the Conversion of Polysulfides to Accelerate the Reaction Kinetics of Lithium-Sulfur Batteries.

More and more attention has been paid to lithium-sulfur (Li─S) batteries due to their high energy density and low cost. However, the intractable "shuttle effect" and the low conductivity of S and its reaction product, Li S, compromise battery performance. To address the inherent challenges, a hollow composite catalyst as a separator coating material is designed, in which CoFe alloy is embedded in a carbon skeleton (CoFeNC@NC).

SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking.

Joint detection and embedding (JDE) methods usually fuse the target motion information and appearance information as the data association matrix, which could fail when the target is briefly lost or blocked in multi-object tracking (MOT). In this paper, we aim to solve this problem by proposing a novel association matrix, the Embedding and GioU (EG) matrix, which combines the embedding cosine distance and GioU distance of objects. To improve the performance of data association, we develop a simple, effective, bottom-up fusion tracker for re-identity features, named SimpleTrack, and propose a new tracking strategy which can mitigate the loss of detection targets.

Hierarchically Hybrid Porous CoO@NiMoO/CoMoO Heterostructures for High-Performance Electrochemical Energy Storage.

Hierarchical, ultrathin, and porous NiMoO@CoMoO on CoO hollow bones were successfully designed and synthesized by a hydrothermal route from the Co-precursor, followed by a KOH (potassium hydroxide) activation process. The hydrothermally synthesized CoO nanowires act as the scaffold for anchoring the NiMoO@CoMoO units but also show more compatibility with NiMoO, leading to high conductivity in the heterojunction. The intriguing morphological features endow the hierarchical CoO@NiMoO@CoMoO better electrochemical performance where the capacity of the CoO@NiMoO@CoMoO heterojunction being 272 mA·h·g at 1 A·g can be achieved with a superior retention of 84.

A flame-retardant polyimide interlayer with polysulfide lithium traps and fast redox conversion towards safety and high sulfur utilization Li-S batteries.

In recent years and following the progress made in lithium-ion battery technology, substantial efforts have been devoted to developing practical lithium-sulfur (Li-S) batteries for next-generation commercial energy storage devices. The practical application of Li-S batteries is still limited by dramatically reduced capacities, cycling instabilities, and safety issues arising from flammable components. In this study, we designed and fabricated a flame-retardant, multifunctional interlayer which integrated electroconductive networks, lithium polysulfide (LiPS) traps and catalysts to significantly elevate the electrochemical performance and safety of pristine Li-S batteries.

High performance flexible asymmetric supercapacitor constructed by cobalt aluminum layered double hydroxide @ nickel cobalt layered double hydroxide heterostructure grown in-situ on carbon cloth.

Hypothesis: Three-dimensional layered layered double hydroxide (LDH) nanostructure materials grow in-situ on excellent conductive and flexible carbon cloth (CC) substrate not only reduce the ability of binders in resisting ions transfer, but also make them to be quasi-vertically arranged well on substrates without aggregation. This would result in enough electroactive sites, to obtain superior electrochemical performance.

Experiments: A hierarchical CoAl-LDH@NiCo-LDH composite was prepared on a surface-modified carbon cloth by a simple two-step hydrothermal process.

Well-designed sophisticated structure of sandwich-like CC@NiAl-LDH@GO@NiCo-LDH material with unique advantages for high performance and practicality hybrid quasi-solid-state supercapacitors.

A sandwich-like flexible architecture electrode material composed of NiAl-LDH nanoplates grown on carbon cloths (CC), coupled with GO interlayer and NiCo-LDH nanowire on the interlayer was successfully assembled via hydrothermal and chemical bath deposition (denoted as CC@NiAl-LDH@GO@NiCo-LDH). The promising combination of NiAl-LDH, graphene and NiCo-LDH forming a multilayer structure through electrostatic absorption and in-situ growth process which endow a high mass loading superiority and synergistic effect for supercapacitors. In addition, the interspace inside the sandwich-like architecture constructed by the graphene and the NiAl-LDH/ NiCo-LDH nano-flakes contribute to alleviate of the volume expansion during the cycling process and promote the diffusion rate of ions.

Brain functional and effective connectivity based on electroencephalography recordings: A review.

Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools.

FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model.

Fisheye images with a far larger Field of View (FOV) have severe radial distortion, with the result that the associated image feature matching process cannot achieve the best performance if the traditional feature descriptors are used. To address this challenge, this paper reports a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor for fisheye images based on a spherical perspective model. Firstly, the 3D gray centroid of feature points is designed, and the position and direction of the feature points on the spherical image are described by a constructed feature point attitude matrix.

LdsConv: Learned Depthwise Separable Convolutions by Group Pruning.

Standard convolutional filters usually capture unnecessary overlap of features resulting in a waste of computational cost. In this paper, we aim to solve this problem by proposing a novel Learned Depthwise Separable Convolution (LdsConv) operation that is smart but has a strong capacity for learning. It integrates the pruning technique into the design of convolutional filters, formulated as a generic convolutional unit that can be used as a direct replacement of convolutions without any adjustments of the architecture.

Nonlinear Modeling of Cortical Responses to Mechanical Wrist Perturbations Using the NARMAX Method.

Objective: Nonlinear modeling of cortical responses (EEG) to wrist perturbations allows for the quantification of cortical sensorimotor function in healthy and neurologically impaired individuals. A common model structure reflecting key characteristics shared across healthy individuals may provide a reference for future clinical studies investigating abnormal cortical responses associated with sensorimotor impairments. Thus, the goal of our study is to identify this common model structure and therefore to build a nonlinear dynamic model of cortical responses, using nonlinear autoregressive-moving-average model with exogenous inputs (NARMAX).

Highly Stable Three-Dimensional Nickel-Cobalt Hydroxide Hierarchical Heterostructures Hybridized with Carbon Nanotubes for High-Performance Energy Storage Devices.

A three-dimensional (3D) composite consisting of nickel-cobalt (Ni-Co) dual hydroxide nanoneedles (NCDHNs) grown on a carbon nanotube (CNT) material, denoted as CNTs@NCDHNs, was designed using a facile one-step hydrothermal method. This composite was further fabricated into electrodes, which exhibited high rate capability and long cycle life. Comparative analysis of the electrochemical performance between 3D CNTs@NCDHNs electrodes and Ni-Co hydroxide electrodes revealed that the high rate capability and long cycle life of the CNTs@NCDHNs are due to a synergistic effect.

A Parametric Time-Frequency Conditional Granger Causality Method Using Ultra-Regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs.

Objective: This study proposes a new parametric time-frequency conditional Granger causality (TF-CGC) method for high-precision connectivity analysis over time and frequency domain in multivariate coupling nonstationary systems, and applies it to source electroencephalogram (EEG) signals to reveal dynamic interaction patterns in oscillatory neocortical sensorimotor networks.

Methods: The Geweke's spectral measure is combined with the time-varying autoregressive with exogenous input (TVARX) modeling approach, which uses multiwavelet-based ultra-regularized orthogonal least squares (UROLS) algorithm, aided by adjustable prediction error sum of squares (APRESS), to obtain high-resolution time-varying CGC representations. The UROLS-APRESS algorithm, which adopts both the regularization technique and the ultra-least squares criterion to measure not only the signal themselves, but also the weak derivatives of them, is a novel powerful method in constructing time-varying models with good generalization performance, and can accurately track smooth and fast changing causalities.

Distractor-Aware Deep Regression for Visual Tracking.

In recent years, regression trackers have drawn increasing attention in the visual-object tracking community due to their favorable performance and easy implementation. The tracker algorithms directly learn mapping from dense samples around the target object to Gaussian-like soft labels. However, in many real applications, when applied to test data, the extreme imbalanced distribution of training samples usually hinders the robustness and accuracy of regression trackers.

A Pilot Study Investigating a Novel Non-Linear Measure of Eyes Open versus Eyes Closed EEG Synchronization in People with Alzheimer’s Disease and Healthy Controls.

Background: The incidence of Alzheimer disease (AD) is increasing with the ageing population. The development of low cost non-invasive diagnostic aids for AD is a research priority. This pilot study investigated whether an approach based on a novel dynamic quantitative parametric EEG method could detect abnormalities in people with AD.

The cortical focus in childhood absence epilepsy; evidence from nonlinear analysis of scalp EEG recordings.

Objective: To determine the origin and dynamic characteristics of the generalised hyper-synchronous spike and wave (SW) discharges in childhood absence epilepsy (CAE).

Methods: We applied nonlinear methods, the error reduction ratio (ERR) causality test and cross-frequency analysis, with a nonlinear autoregressive exogenous (NARX) model, to electroencephalograms (EEGs) from CAE, selected with stringent electro-clinical criteria (17 cases, 42 absences). We analysed the pre-ictal and ictal strength of association between homologous and heterologous EEG derivations and estimated the direction of synchronisation and corresponding time lags.

Time-Varying System Identification Using an Ultra-Orthogonal Forward Regression and Multiwavelet Basis Functions With Applications to EEG.

A new parametric approach is proposed for nonlinear and nonstationary system identification based on a time-varying nonlinear autoregressive with exogenous input (TV-NARX) model. The TV coefficients of the TV-NARX model are expanded using multiwavelet basis functions, and the model is thus transformed into a time-invariant regression problem. An ultra-orthogonal forward regression (UOFR) algorithm aided by mutual information (MI) is designed to identify a parsimonious model structure and estimate the associated model parameters.

Quantifying heterogeneous responses of fish community size structure using novel combined statistical techniques.

To understand changes in ecosystems, the appropriate scale at which to study them must be determined. Large marine ecosystems (LMEs) cover thousands of square kilometres and are a useful classification scheme for ecosystem monitoring and assessment. However, averaging across LMEs may obscure intricate dynamics within.

A nonlinear generalization of spectral Granger causality.

Spectral measures of linear Granger causality have been widely applied to study the causal connectivity between time series data in neuroscience, biology, and economics. Traditional Granger causality measures are based on linear autoregressive with exogenous (ARX) inputs models of time series data, which cannot truly reveal nonlinear effects in the data especially in the frequency domain. In this study, it is shown that the classical Geweke's spectral causality measure can be explicitly linked with the output spectra of corresponding restricted and unrestricted time-domain models.

A nonlinear causality measure in the frequency domain: nonlinear partial directed coherence with applications to EEG.

Background: Frequency domain Granger causality measures have been proposed and widely applied in analyzing rhythmic neurophysiological and biomedical signals. Almost all these measures are based on linear time domain regression models, and therefore can only detect linear causal effects in the frequency domain.

New Method: A frequency domain causality measure, the partial directed coherence, is explicitly linked with the frequency response function concept of linear systems.

Quantitative EEG analysis using error reduction ratio-causality test; validation on simulated and real EEG data.

Objective: To introduce a new method of quantitative EEG analysis in the time domain, the error reduction ratio (ERR)-causality test. To compare performance against cross-correlation and coherence with phase measures.

Methods: A simulation example was used as a gold standard to assess the performance of ERR-causality, against cross-correlation and coherence.

A parametric method to measure time-varying linear and nonlinear causality with applications to EEG data.

A linear and nonlinear causality detection method called the error-reduction-ratio causality (ERRC) test is introduced in this paper to investigate if linear or nonlinear models should be considered in the study of human electroencephalograph (EEG) data. In comparison to the traditional Granger methods, one significant advantage of the ERRC approach is that it can effectively detect the time-varying linear and nonlinear causalities between two signals without fitting a complete nonlinear model. Two numerical simulation examples are employed to compare the performance of the new method with other widely used methods in the presence of noise and in tracking time-varying causality.