Yolk-shell FeS@N-doped carbon nanosphere as superior anode materials for sodium-ion batteries.

Iron sulfides have shown great potential as anode materials for sodium-ion batteries (SIBs) because of their high sodium storage capacity and low cost. Nevertheless, iron sulfides generally exhibit unsatisfied electrochemical performance induced by sluggish electron/ion transfer and severe pulverization upon the sodiation/desodiation process. Herein, we constructed a yolk-shell FeS@NC nanosphere with an N-doped carbon shell and FeS particle core via a simple hydrothermal method, followed by in-situ polymerization and vulcanization. The FeS particles intimately coupled with N-doped carbon can accelerate the electron transfer, avoid severe volume expansion, and maintain structural stability upon repeated sodiation/desodiation process. Furthermore, the small particle size of FeS can shorten ion-diffusion distance and facilitate ion transportation. Therefore, the FeS@NC nanosphere shows excellent cycling performance and superior rate capability that it can deliver a high capacity of 520.1 mAh g-1 over 800 cycles at 2.0 A g-1 and a remarkable capacity of 625.9 mAh g-1 at 10.0 A g-1.

Interface engineering of metal sulfides-based composites enables high-performance anode materials for sodium-ion batteries.

Metal sulfides (MSs) have attracted much attention as anode materials for sodium-ion batteries (SIBs) due to their high sodium storage capacity. However, the unsatisfactory electrochemical performance induced by the huge volume change and sluggish kinetics hampered the practical application of SIBs. Herein, guided by the heterostructure interface engineering, novel multicomponent metal sulfide-based anodes, including SnS, FeS, and Fe3N embedded in N-doped carbon nanosheets (SnS/FeS/Fe3N/NC NSs), have been synthesized for high-performance SIBs. The as-prepared SnS/FeS/Fe3N/NC NSs with abundant heterointerfaces and high conductivity of N-doped carbon nanosheet matrix can shorten the Na+ diffusion path and promote reaction kinetics during the sodiation/desodiation process. Moreover, the presence of Fe3N can promote the reversible conversion of SnS and FeS during the cycling process. As a consequence, when evaluated as anode materials for SIBs, the SnS/FeS/Fe3N/NC NSs can maintain a high sodium storage capacity of 473.6 mAh g-1 after 600 cycles at 2.0 A g-1 and can still provide a high reversible capacity of 537.4 mAh g-1 even at 5.0 A g-1 This discovery offers a novel strategy for constructing metal sulfide-based anode materials for high-performance SIBs.

Cross-subject emotion recognition using hierarchical feature optimization and support vector machine with multi-kernel collaboration.

Objective. Due to individual differences, it is greatly challenging to realize the multiple types of emotion identification across subjects.Approach. In this research, a hierarchical feature optimization method is proposed in order to represent emotional states effectively based on peripheral physiological signals. Firstly, sparse learning combined with binary search is employed to achieve feature selection of single signals. Then an improved fast correlation-based filter is proposed to implement fusion optimization of multi-channel signal features. Aiming at overcoming the limitations of the support vector machine (SVM), which uses a single kernel function to make decisions, the multi-kernel function collaboration strategy is proposed to improve the classification performance of SVM.Main results. The effectiveness of the proposed method is verified on the DEAP dataset. Experimental results show that the proposed method presents a competitive performance for four cross-subject  types of emotion identification with an accuracy of 84% (group 1) and 85.07% (group 2). Significance. The proposed model with hierarchical feature optimization and SVM with multi-kernel function collaboration demonstrates superior emotion recognition accuracy compared to state-of-the-art techniques. In addition, the analysis based on DEAP dataset composition characteristics presents a novel perspective to explore the emotion recognition issue more objectively and comprehensively.

[Research on the feature representation of motor imagery electroencephalogram signal based on individual adaptation].

Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.

Characterizing the effects of contributing factors on crash severity involving e-bicycles: a study based on police-reported data.

Mitigating e-bicycle crash occurrence has become a great challenge across the world. It is of paramount importance for improving traffic safety to characterize the relationship between e-bicycle crash injury severities and contributing factors. This study positions itself at clarifying the roles of the factors in e-bicycle crashes from time, space, road, environment, rider and object characteristics. The partial proportional odds (PPOs) model as well as its elasticity analysis was employed to identify the influences based on 15,138 police-reported e-bicycle crashes in Shangyu District of Shaoxin City, China. The results evidenced that there were 12 factors having significant effects. Especially, the results emphasized the greater influences of rider gender, age, object hit and road type. Their maximum of the absolutes of elasticities was greater than 24%. Increased crash severity was associated with females, younger riders, and higher speed collisions. However, the remaining significant variables had minor effects (no more than 10%). The findings provide meaningful insights for advancing e-bicycle development, when making related policies and prioritizing safety countermeasures.