A Tri-Band Low-Profile High-Gain Planar Antenna Using Fabry-Perot Cavity

A tri-band high-gain antenna with a planar structure and low profile is proposed. The principle of operation is explained. It is based on Fary-Perot cavity antenna (FPCA) with two frequency selective surface (FSS) layers. Two different resonant frequencies are generated by the two resonant cavities formed by the ground plane and each of the two FSS layers, respectively. A third resonant frequency is produced by combining the two FSS layers together. Advantages of this tri-band antenna includes low profile, high gain, easy fabrication and low cost. Low profile is achieved by designing the combined FSS layers as an artificial magnetic conductor (AMC) with a reflection coefficient having 0o phase shift and high magnitude. In addition, a large frequency ratio, which is often a problem for multiband array antennas, can be achieved here. To verify this concept, a C/X/Ku band FPCA is designed and one prototype is fabricated and tested. Experimental results agree well with the simulated results. High gain performance with good impedance matching in three bands is obtained, which reaches a peak gain of 14.2 dBi at 5.2 GHz, 18.9 dBi at 9.6 GHz and 19.8 dBi at 14.7 GHz, respectively. The overall height of antenna is only 20.2 mm, which is about 1/3 wavelength at its lowest operating frequency, which means a reduction of 30% compared to the height of traditional FPCA antenna

Inhibition of p38 MAPK Signaling Regulates the Expression of EAAT2 in the Brains of Epileptic Rats

Seizures induce the release of excitatory amino acids (EAAs) from the intracellular fluid to the extracellular fluid, and the released EAAs primarily comprise glutamic acid (Glu) and asparaginic acid (Asp). Glu neurotransmission functions via EAA transporters (EAATs) to maintain low concentrations of Glu in the extracellular space and avoid excitotoxicity. EAAT2, the most abundant Glu transporter subtype in the central nervous system (CNS), plays a key role in the regulation of glutamate transmission. Previous studies have shown that SB203580 promotes EAAT2 expression by inhibiting the p38 mitogen-activated protein kinase (MAPK) signaling pathway, but whether SB203580 upregulates EAAT2 expression in epileptic rats is unknown. This study demonstrated that EAAT2 expression was increased in the brain tissue of epileptic rats. Intraperitoneal injection of a specific inhibitor of p38 MAPK, SB203580, reduced the time to the first epileptic seizure and attenuated the seizure severity. In addition, SB203580 treatment increased the EAAT2 expression levels in the brain tissue of epileptic rats. These results suggest that SB203580 could regulate epileptic seizures via EAAT2

Differential Optimization Federated Incremental Learning Algorithm Based on Blockchain

Federated learning is a hot area of concern in the field of privacy protection. There are local model parameters that are difficult to integrate, poor model timeliness, and local model training security issues. This paper proposes a blockchain-based differential optimization federated incremental learning algorithm, First, we apply differential privacy to the weighted random forest and optimize the parameters in the weighted forest to reduce the impact of adding differential privacy on the accuracy of the local model. Using different ensemble algorithms to integrate the local model parameters can improve the accuracy of the global model. At the same time, the risk of a data leakage caused by gradient update is reduced; then, incremental learning is applied to the framework of federated learning to improve the timeliness of the model; finally, the model parameters in the model training phase are uploaded to the blockchain and synchronized quickly, which reduces the cost of data storage and model parameter transmission. The experimental results show that the accuracy of the stacking ensemble model in each period is above 83.5% and the variance is lower than 10−4 for training on the public data set. The accuracy of the model has been improved, and the security and privacy of the model have been improved

Differential Optimization Federated Incremental Learning Algorithm Based on Blockchain

Federated learning is a hot area of concern in the field of privacy protection. There are local model parameters that are difficult to integrate, poor model timeliness, and local model training security issues. This paper proposes a blockchain-based differential optimization federated incremental learning algorithm, First, we apply differential privacy to the weighted random forest and optimize the parameters in the weighted forest to reduce the impact of adding differential privacy on the accuracy of the local model. Using different ensemble algorithms to integrate the local model parameters can improve the accuracy of the global model. At the same time, the risk of a data leakage caused by gradient update is reduced; then, incremental learning is applied to the framework of federated learning to improve the timeliness of the model; finally, the model parameters in the model training phase are uploaded to the blockchain and synchronized quickly, which reduces the cost of data storage and model parameter transmission. The experimental results show that the accuracy of the stacking ensemble model in each period is above 83.5% and the variance is lower than 10−4 for training on the public data set. The accuracy of the model has been improved, and the security and privacy of the model have been improved

Characterization of the first complete chloroplast genome of Amaranthus hybridus (Caryophyllales: Amaranthaceae) with phylogenetic implications

In the present study, the complete chloroplast genome of Amaranthus hybridus was sequenced and assembled. The complete chloroplast genome of Amaranthus hybridus is 150,709 in size, with the GC content of 36.56%. The chloroplast genome of Amaranthus hybridus contained 86 protein-coding genes (PCGs), eight ribosomal RNA (rRNA) genes, and 37 transfer RNA (tRNA) genes. Phylogenetic analysis based on combined chloroplast gene dataset indicated that the Amaranthus hybridus exhibited a close relationship with A. hypochondriacus and A. caudatus

Valproate encephalopathy: Case series and literature review

Valproate encephalopathy is one of the unusual and severe but treatable side effect. This research focuses on four female patients who had valproate medication for epilepsy and developed an increased frequency of seizures, exacerbated disruption of consciousness, gastrointestinal problems, cognitive dysfunction, ataxia, and psychobehavioral abnormalities. The patient’s symptoms improved over time once sodium valproate was stopped. As a result, when using sodium valproate, one should be aware of the risk of sodium valproate encephalopathy and cease using the medication right once if any of the above symptoms of unknown etiology manifest clinically. We also go over the potential pathogenesis that lead to valproate encephalopathy and the heightened risk of encephalopathy from taking antiepileptic medications together. It was stressed how crucial it is to identify, diagnose, and treat sodium valproate encephalopathy as soon as possible

Intelligent augmented keyword search on spatial entities in real-life internet of vehicles

Internet of Vehicles (IoV) has attracted wide attention from both academia and industry. Due to the popularity of the geographical devices deployed on the vehicles, a tremendous amount of spatial entities which include spatial information, unstructured information and structured information, are generated every second. This development calls for intelligent augmented spatial keyword queries (ASKQ), which intelligently takes into account the locations, unstructured information (in the form of keyword sets), structured information (in the form of boolean expressions) of 182MinzuAvespatial entities. In this paper, we take the first step to address the issue of processing ASKQ in real traffic networks of IoV environments (ASKQIV) and focus on Top-k ASKQIV queries. To support network distance pruning, keyword pruning, and boolean expression pruning intelligently and simultaneously, a novel hybrid index structure called ASKTI is proposed. Note in the real-life traffic networks of IoV environments, travel cost is not only decided by the network distance, but also decided by some additional travel factors. By considering these additional factors, a combined factor Cftc of each road (edge) in the traffic network of IoV environments is calculated, and weighted network distance is calculated and adopted. Based on ASKTI, an efficient algorithm for Top-k ASKQIV query processing is proposed. Our method can also be extended to handle boolean range ASKQIV Queries and ranking ASKQIV Queries. Finally, simulation experiments on one real traffic network of IoV environments and two synthetic spatial entity sets are conducted. The results show that our ASKTI based method is superior to its competitors.University of Derb