Search

This paper presents a design to realize extremely low cross‐polarization level in multibeam antenna. The antenna consists of two reflectors placed on top of each other. The front reflector is a panel made of metal grid, which is used to separate two orthogonal polarizations. The rear reflector serves as the actual multibeam antenna surface. The multibeam antenna and feed array operated at Ku band are designed and fabricated. The test results show that the gains of the 28 beams can be better than 40.42 dBi, and the cross‐polarization level can be lower than −40 dB. The proposed method provides a novel idea for the design of ultralow cross‐polarization multibeam antenna. [ABSTRACT FROM AUTHOR]

Named entity recognition (NER) is a subfield of natural language processing (NLP) that identifies and classifies entities from plain text, such as people, organizations, locations, and other types. NER is a fundamental task in information extraction, information retrieval, and text summarization, as it helps to organize the relevant information in a structured way. The current approaches to Chinese named entity recognition do not consider the category information of matched Chinese words, which limits their ability to capture the correlation between words. This makes Chinese NER more challenging than English NER, which already has well-defined word boundaries. To improve Chinese NER, it is necessary to develop new approaches that take into account category features of matched Chinese words, and the category information would help to effectively capture the relationship between words. This paper proposes a Prompt-based Word-level Information Injection BERT (PWII-BERT) to integrate prompt-guided lexicon information into a pre-trained language model. Specifically, we engineer a Word-level Information Injection Adapter (WIIA) through the original Transformer encoder and prompt-guided Transformer layers. Thus, the ability of PWII-BERT to explicitly obtain fine-grained character-to-word relevant information according to the category prompt is one of its key advantages. In experiments on four benchmark datasets, PWII-BERT outperforms the baselines, demonstrating the significance of fully utilizing the advantages of fusing the category information and lexicon feature to implement Chinese NER. [ABSTRACT FROM AUTHOR]

Assessing the terrestrial vegetation carbon density (TVCD) is crucial for evaluating the national carbon balance. However, current national‐scale TVCD assessments show strong disparities, despite the good estimation method of their underlying models. Here, we attribute this contradiction to a flaw in the methods of using multimodel simulation results, which ignore the connections between results, leading to an overoptimistic evaluation of the multimodel ensemble mean (MMEM) method.Thus, using the state‐of‐the‐art multimodel random forest ensemble (MMRFE) method to integrate the results of 10 models, we reproduced Chinese TVCD data during 1982–2010.Compared with the nationally averaged TVCD field investigation data (27 ± 26 Mg C/ha), we found that the results of five models were overestimated by 7.4%–85.2%, and the remaining models were underestimated by 3.7%–77.8%. The MMEM TVCD method produced an overestimation of 2%, but the MMRFE method produced an underestimation of only 0.2%. Additionally, the summary Taylor diagrams of the TVCD at the national and ecosystem (forest, shrub, grass and crop ecosystems) scales all showed that the MMRFE TVCD produced the smallest standard deviations and root mean square deviations and the highest correlation coefficients. Furthermore, the MMRFE TVCDs were all significantly positively correlated with the normalized difference vegetation index (NDVI), and they had the same increasing trend, but an opposite variation trend from the MMEM TVCD and NDVI. This result implied that the spatiotemporal variation modes of the MMRFE TVCD were consistent with those of the NDVI. The results suggested that compared with the traditional MMEM method, the MMRFE TVCD and its spatiotemporal variation modes were more similar to the real TVCD.In conclusion, the MMRFE method can effectively improve the accuracy of national‐scale TVCD estimation, and effectively reduce the uncertainty of large‐scale terrestrial vegetation carbon estimation processes. Notably, we provide a new method that uses a machine learning approach to mine multimodel terrestrial carbon information to reduce the uncertainty in the estimation of terrestrial ecosystem carbon components. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) indicates the occurrence of various cardiac diseases, and the accurate classification of ECG signals is important for the automatic diagnosis of arrhythmia. This paper presents a novel classification method based on multiple features by combining waveform morphology and frequency domain statistical analysis, which offer improved classification accuracy and minimise the time spent for classifying signals. A wavelet packet is used to decompose a denoised ECG signal, and the singular value, maximum value, and standard deviation of the decomposed wavelet packet coefficients are calculated to obtain the frequency domain feature space. The slope threshold method is applied to detect R peak and calculate RR intervals, and the first two RR intervals are extracted as time-domain features. The fusion feature space is composed of time and frequency domain features. A combination of support vector machine (SVM) with the help of grid search and waveform morphological analysis is applied to complete nine types of ECG signal classification. Computer simulations show that the accuracy of the proposed algorithm on multiple types of arrhythmia databases can reach 96.67%. [ABSTRACT FROM AUTHOR]

Background. The association between heart failure (HF) and cognitive impairment has received increasing attention from scholars and researchers in recent years. However, no systematic studies have been carried out yet focused on the crosstalk between heart failure and cognitive impairment via miRNAs. Methods. GSE104150, GSE53473, GSE120584, and GSE116250 with RNA-seq data and clinical data were downloaded from the GSE database. All data were statistically analysed using R software to detect DE-miRNAs and DE-mRNAs associated with both HF and cognitive impairment. Protein-protein interaction (PPI) networks were mapped, and a logistic regression model for cognitive impairment prediction was developed. Furthermore, the TTRUST database and miRWalk were used to map miRNA-transcription factor (TF) and messenger RNA (mRNA) regulatory pathways. Finally, core TFs were enriched for analysis. Results. Differentially enriched DE-miRNAs and DE-mRNAs both present in HF and cognitive impairment were determined. A logistic regression model established based on DE-miRNAs was validated to have a strong performance in cognitive impairment prediction. The core miRNA-TF-mRNA pathway was formed by mapping the PPI networks associated with the two diseases. Further GSEA was performed with V-rel reticuloendotheliosis viral oncogene homolog B (RELB) as the core TF, and the retinol metabolism and gap junction pathways were analysed. Conclusions. This study was the first attempt to predict the crosstalk and examine underlying mechanisms between HF and cognitive impairment applying bioinformatics. The findings suggested a potential hsa-miR-933/RELB/CCL21 regulatory axis correlated with HF and neurological disorders (or cognitive impairment), according to PPI networks. [ABSTRACT FROM AUTHOR]

Magnesium oxide/expanded graphite (MgO/EG) catalyst was synthesized and applied for enhancing the degradation of Cu-ethylenediaminetetraacetic acid (Cu-EDTA) in an aqueous solution. The MgO/EG catalyst was characterized by XRD, SEM, EDS, and FTIR. For assessing the catalytic activity of MgO/EG, essential influencing factors were investigated including catalyst dosage, O3 dosage, initial pH, initial Cu-EDTA concentration, and coexisting ions. The results show that the catalytic material showed high catalytic oxidation capacity for the Cu-EDTA removal in the MgO/EG/O3 system. 100% of Cu(II) and 73.2% of TOC removal efficiency could be achieved in the MgO/EG/O3 system at the reaction times of 90 min. This efficiency was higher than that seen for other systems, including O3 alone (Cu(II) 81.4%/TOC 60.6%), EG/O3 (84.2%/64.1), MgO/EG (< 4%/< 4%), and EG (< 4%/< 4%). A small decrease in the Cu(II) and TOC removal rate was observed after three runs in the stability and reusability experiments of the catalyst. Assays with radical scavenging experiments confirmed that MgO/EG-mediated oxidation was dependent on a hydroxyl radical pathway. The UV–vis spectra confirmed that the absorption peak of Cu-EDTA was gradually decreased and finally disappeared. [ABSTRACT FROM AUTHOR]

A novel calcium carbonate (CaCO3)/expanded graphite (EG) catalyst was fabricated, characterized, and used in a catalytic ozonation process to increase degradation and mineralization of ethylenediaminetetraacetic acid (EDTA) aqueous solutions. Various techniques were used to analyze the crystalline structure, morphology, and elemental composition of the novel CaCO3/EG catalyst, including X-ray diffraction, scanning electron microscopy, and energy dispersive spectrometry. Mercury intrusion porosimetry was used to measure specific surface area, pore diameter, and pore volume of the CaCO3/EG catalyst. Essential influencing factors such as CaCO3/EG composite dosage and initial pH were also surveyed for different systems. Catalytic ozonation by CaCO3/EG was notably increased by the removal of total organic carbon (TOC) of EDTA. After 60 min, 80% TOC removal efficiency from simulated wastewater having an initial TOC concentration of 600 mg/L and initial pH of 4.9 was achieved using 1.0 g/L CaCO3/EG. This efficiency was higher than that seen for other systems, including O3 alone (40%), EG/O3 (39%), CaCO3/O3 (28%), and EG (4%). A small decrease in the CaCO3/EG-catalyzed TOC removal rate was observed after three runs, but nonetheless the efficiency was >80% after 120 min. Assays with tert-butanol confirmed that CaCO3/EG-mediated catalytic ozonation was dependent on a free radical pathway. The results support that CaCO3/EG material could serve as an important reusable resource for catalytic oxidation reactions during treatment of wastewater containing EDTA. [ABSTRACT FROM AUTHOR]

This paper proposes the equivalent strip width for stiffness-driven design of fiber-reinforced polymer (FRP) superstructures in slab-type bridges. To calculate the deflections for design, this paper first presents a tractable and accurate analytical solution for FRP superstructures under certain symmetry in accordance with classical laminate plate theory. The equivalent strip width can then be obtained by equating beam flexural deflections to the analytical solution. Through the application of the equivalent strip width, two-dimensional FRP superstructures can be simplified as one-dimensional Timoshenko beams. In accordance with the parametric studies from finite-element analysis, Timoshenko beam approximation with the equivalent strip width can predict the deflections at points of interest in practical design with sufficient accuracy. At the design stage of FRP superstructures, several material and section properties required by typical plate analysis remain unknown to the designers. This paper shows that the simplified equivalent strip width with proper assumptions about a few key parameters can avoid this practical inconvenience and facilitate design. A procedure for the design of FRP superstructures is proposed and a design example is shown. [ABSTRACT FROM AUTHOR]

This paper presents the experimental study about the behavior of honeycomb fiber-reinforced polymer (HFRP) sandwich structure with corrugated cores under the combined effects of service load and low-temperature cycling (ranging from 24°C to -35°C). The potential debonding at the interfaces between the corrugated cores and face sheets because of the service-load condition specific to bridge engineering at low temperatures and its impact on stiffness are studied in this paper. The finite-element analysis (FEA) was utilized to determine the load in the experiment. The experiment consisted of tests conducted at four different temperatures. The load-strain responses were monitored to study the behaviors of HFRP sandwich panels. On the basis of the observation and results from the experiment, this paper concludes that the deflection limit span over 400 can potentially be adopted in practice without incurring stiffness degradation because of interface debonding. The paper also shows that it is essential to study the tolerable size of cracks or defects at the interfaces before applying this deflection limit as a design criterion. The experimental results in this study serve as a necessary supplement to the material tests and confirm that the stiffness of the HFRP sandwich panels at the structural level will also increase when the temperatures are decreased at least up to the service-limit state. Finally, this paper verifies the important role that tensile stress plays in interface debonding and suggests that previous tests may underestimate the actual shear strength of the interfaces. [ABSTRACT FROM AUTHOR]

Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%. [ABSTRACT FROM AUTHOR]

Cancer immunotherapy relies upon the ability of T cells to infiltrate tumors. The endothelium constitutes a barrier between the tumor and effector T cells, and the ability to manipulate local vascular permeability could be translated into effective immunotherapy. Here, we show that in the context of adoptive T cell therapy, antitumor T cells, delivered at high enough doses, can overcome the endothelial barrier and infiltrate tumors, a process that requires local production of C3, complement activation on tumor endothelium and release of C5a. C5a, in turn, acts on endothelial cells promoting the upregulation of adhesion molecules and T-cell homing. Genetic deletion of C3 or the C5a receptor 1 (C5aR1), and pharmacological blockade of C5aR1, impaired the ability of T cells to overcome the endothelial barrier, infiltrate tumors, and control tumor progressionin vivo, while genetic chimera mice demonstrated that C3 and C5aR1 expression by tumor stroma, and not leukocytes, governs T cell homing, acting on the local endothelium.In vitro, endothelial C3 and C5a expressions were required for endothelial activation by type 1 cytokines. Our data indicate that effective immunotherapy is a consequence of successful homing of T cells in response to local complement activation, which disrupts the tumor endothelial barrier. [ABSTRACT FROM PUBLISHER]