Selective Shielding of the (002) Plane Enabling Vertically Oriented Zinc Plating for Dendrite-Free Zinc Anode.

Uncontrolled growth of Zn dendrites hinders the future development of aqueous Zn-ion batteries. Despite that the (100) plane possesses better zincophilic ability and fast kinetics, dendrites are generally suppressed via (002) plane-oriented Zn deposition in previous reports; the ordered (100) plane-dominant Zn deposition, especially under high current density has not yet been realized. Herein, vertically-oriented Zn plating with preferential growth of (100) plane is reported using disodium lauryl phosphate (DLP) as an electrolyte additive. DLP is preferentially anchored on the Zn (002) crystal plane via the polar phosphate group, then the deposition of Zn atoms on the (002) plane is retarded by the long alkyl chain, finally promoting the preferred growth of the (100) plane. This unique growth pattern results in ultrastable Zn plating/stripping at a super-high current density of 50 mA cm-2 , with a cumulative capacity of 8500 mAh cm-2 . The Zn//Zn symmetric cell also cycles steadily for 700 h with a large areal capacity of 10 mAh cm-2 at a current density of 10 mA cm-2 . This study provides new insights into the realization of dendrite-free Zn anodes by crystal plane modulation.

A Pedestrian Detection Network Model Based on Improved YOLOv5.

Advanced object detection methods always face high algorithmic complexity or low accuracy when used in pedestrian target detection for the autonomous driving system. This paper proposes a lightweight pedestrian detection approach called the YOLOv5s-G2 network to address these issues. We apply Ghost and GhostC3 modules in the YOLOv5s-G2 network to minimize computational cost during feature extraction while keeping the network's capability of extracting features intact. The YOLOv5s-G2 network improves feature extraction accuracy by incorporating the Global Attention Mechanism (GAM) module. This application can extract relevant information for pedestrian target identification tasks and suppress irrelevant information, improving the unidentified problem of occluded and small targets by replacing the GIoU loss function used in the bounding box regression with the α-CIoU loss function. The YOLOv5s-G2 network is evaluated on the WiderPerson dataset to ensure its efficacy. Our proposed YOLOv5s-G2 network offers a 1.0% increase in detection accuracy and a 13.2% decrease in Floating Point Operations (FLOPs) compared to the existing YOLOv5s network. As a result, the YOLOv5s-G2 network is preferable for pedestrian identification as it is both more lightweight and more accurate.

Multi-Classifier Fusion Based on MI-SFFS for Cross-Subject Emotion Recognition.

With the widespread use of emotion recognition, cross-subject emotion recognition based on EEG signals has become a hot topic in affective computing. Electroencephalography (EEG) can be used to detect the brain's electrical activity associated with different emotions. The aim of this research is to improve the accuracy by enhancing the generalization of features. A Multi-Classifier Fusion method based on mutual information with sequential forward floating selection (MI_SFFS) is proposed. The dataset used in this paper is DEAP, which is a multi-modal open dataset containing 32 EEG channels and multiple other physiological signals. First, high-dimensional features are extracted from 15 EEG channels of DEAP after using a 10 s time window for data slicing. Second, MI and SFFS are integrated as a novel feature-selection method. Then, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) are employed to classify positive and negative emotions to obtain the output probabilities of classifiers as weighted features for further classification. To evaluate the model performance, leave-one-out cross-validation is adopted. Finally, cross-subject classification accuracies of 0.7089, 0.7106 and 0.7361 are achieved by the SVM, KNN and RF classifiers, respectively. The results demonstrate the feasibility of the model by splicing different classifiers' output probabilities as a portion of the weighted features.

A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals.

Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen's kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.

Hereditary protein S deficiency leads to ischemic stroke.

Hereditary protein S (PS) deficiency is an independent risk factor for venous thromboembolism. However, the correlation between PS and arterial thrombotic disease, such as cerebral thrombosis, is not clear. The present study focused on the molecular mechanisms underlying ischemic stroke caused by a PS gene mutation in one family. The activity of antithrombin, protein C and PS in the plasma of the proband was measured, and the genes encoding PS were amplified and sequenced. The cellular localization and expression of PS were analyzed in HEK­293 cells. The proband was a 50­year­old male. Plasma PS activity of the proband was 38.9%, which was significantly decreased compared with normal levels. Sequencing analysis revealed a PROS1 c.1486_1490delGATTA mutation on exon 12. This frameshift mutation converts Asp496 in the precursor PS into the termination codon. In addition, the PROS1 mutation was correlated with low PS activity in the family. Functional tests revealed that the mutant protein aggregated in the cytoplasm and its secretion and expression decreased. In conclusion, protein S mutation appeared to be the primary cause of thrombosis in the family of the present study. However, the correlation between PS deficiency and ischemic stroke requires further investigation.