Improved Motor Imagery Classification Using Adaptive Spatial Filters Based on Particle Swarm Optimization Algorithm

As a typical self-paced brain-computer interface (BCI) system, the motor imagery (MI) BCI has been widely applied in fields such as robot control, stroke rehabilitation, and assistance for patients with stroke or spinal cord injury. Many studies have focused on the traditional spatial filters obtained through the common spatial pattern (CSP) method. However, the CSP method can only obtain fixed spatial filters for specific input signals. Besides, CSP method only focuses on the variance difference of two types of electroencephalogram (EEG) signals, so the decoding ability of EEG signals is limited. To obtain more effective spatial filters for better extraction of spatial features that can improve classification to MI-EEG, this paper proposes an adaptive spatial filter solving method based on particle swarm optimization algorithm (PSO). A training and testing framework based on filter bank and spatial filters (FBCSP-ASP) is designed for MI EEG signal classification. Comparative experiments are conducted on two public datasets (2a and 2b) from BCI competition IV, which show the outstanding average recognition accuracy of FBCSP-ASP. The proposed method has achieved significant performance improvement on MI-BCI. The classification accuracy of the proposed method has reached 74.61% and 81.19% on datasets 2a and 2b, respectively. Compared with the baseline algorithm (FBCSP), the proposed algorithm improves 11.44% and 7.11% on two datasets respectively. Furthermore, the analysis based on mutual information, t-SNE and Shapley values further proves that ASP features have excellent decoding ability for MI-EEG signals, and explains the improvement of classification performance by the introduction of ASP features.Comment: 25 pages, 8 figure

A Novel Compact Quadruple-Band Indoor Base Station Antenna for 2G/3G/4G/5G Systems

This paper presents a quadruple-band indoor base station antenna for 2G/3G/4G/5G mobile communications, which covers multiple frequency bands of 0.8 - 0.96 GHz, 1.7 - 2.7 GHz, 3.3 - 3.8 GHz and 4.8 - 5.8 GHz and has a compact size with its overall dimensions of 204 × 175 × 39 mm 3 . The lower frequency bands over 0.8 - 0.96 GHz and 1.7 - 2.7 GHz are achieved through the combination of an asymmetrical dipole antenna and parasitic patches. A stepped-impedance feeding structure is used to improve the impedance matching of the dipole antenna over these two frequency bands. Meanwhile, the feeding structure also introduces an extra resonant frequency band of 3.3 - 3.8 GHz. By adding an additional small T-shaped patch, the higher resonant frequency band at 5 GHz is obtained. The parallel surrogate model-assisted hybrid differential evolution for antenna optimization (PSADEA) is employed to optimize the overall quadruple-band performance. We have fabricated and tested the final optimized antenna whose average gain is about 5.4 dBi at 0.8 - 0.96 GHz, 8.1 dBi at 1.7 - 2.7 GHz, 8.5 dBi at 3.3 - 3.8 GHz and 8.1 dBi at 4.8 - 5.0 GHz respectively. The proposed antenna has high efficiency and is of low cost and low profile, which makes it an excellent candidate for 2G/3G/4G/5G base station antenna systems

Harvested Wood Products as a Carbon Sink in China, 1900–2016

The use of harvested wood products (HWPs) influences the carbon flux. China is both the major producer and trader of HWP, so estimating the carbon stock change of China’s HWP is important to help curb climate change. Accurate reporting and accounting of carbon flows in the HWP pool is needed to meet greenhouse gas monitoring and climate change mitigation objectives under the United Nations Framework Convention on Climate Change (UNFCCC) and the Paris Agreement. This study applied production approach (PA) to estimate the carbon stock change of China’s HWP from 1900 to 2016. During the estimating period, the carbon stock of HWP in use and deposed at solid waste disposal sites (SWDS) were 649.2 Teragrams Carbon (TgC) (346.8 TgC in wood-based panels, 216.7 TgC in sawnwood and 85.7 TgC in paper & paperboard) and 72.6 TgC, respectively. The carbon amount of annual domestic harvest HWP varied between 87.6 and 118.7 TgC. However, the imported carbon inflow increased significantly after the 1990s and reached 47.6 TgC in 2016, accounting for 46% of the domestic harvest of that year. China has great mitigation potential from HWP and use of this resource should be considered in future strategies to address climate change

Identification of a Novel Epithelial–Mesenchymal Transition-Related Gene Signature for Endometrial Carcinoma Prognosis

(1) Background: Endometrial cancer is the most prevalent cause of gynecological malignant tumor worldwide. The prognosis of endometrial carcinoma patients with distant metastasis is poor. (2) Method: The RNA-Seq expression profile and corresponding clinical data were downloaded from the Cancer Genome Atlas database and the Gene Expression Omnibus databases. To predict patients’ overall survival, a 9 EMT-related genes prognosis risk model was built by machine learning algorithm and multivariate Cox regression. Expressions of nine genes were verified by RT-qPCR. Responses to immune checkpoint blockades therapy and drug sensitivity were separately evaluated in different group of patients with the risk model. (3) Endometrial carcinoma patients were assigned to the high- and low-risk groups according to the signature, and poorer overall survival and disease-free survival were showed in the high-risk group. This EMT-related gene signature was also significantly correlated with tumor purity and immune cell infiltration. In addition, eight chemical compounds, which may benefit the high-risk group, were screened out. (4) Conclusions: We identified a novel EMT-related gene signature for predicting the prognosis of EC patients. Our findings provide potential therapeutic targets and compounds for personalized treatment. This may facilitate decision making during endometrial carcinoma treatment