Optimal Design of Aperture Illuminations for Microwave Power Transmission with Annular Collection Areas

This work presents an optimal design method of antenna aperture illumination for microwave power transmission with an annular collection area.The objective is to maximize the ratio of the power radiated on the annular collection area to the total transmitted power.By formulating the aperture amplitude distribution through a summation of a special set of series,the optimal design problem can be reduced to finding the maximum ratio of two real quadratic forms.Based on the theory of matrices,the solution to the formulated optimization problem is to determine the largest characteristic value and its associated characteristic vector.To meet security requirements,the peak radiation levels outside the receiving area are considered to be extra constraints.A hybrid grey wolf optimizer and Nelder–Mead simplex method is developed to deal with this constrained optimization problem.In order to demonstrate the effectiveness of the proposed method,numerical experiments on continuous apertures are conducted;then,discrete arrays of isotropic elements are employed to validate the correctness of the optimized results.Finally,patch arrays are adopted to further verify the validity of the proposed method.

Scavenging Microwave Wireless Power:A Unified Model,Rectenna Design Automation,and Cutting-Edge Techniques

While sufficient review articles exist on inductive short-range wireless power transfer(WPT),long-haul microwave WPT(MWPT)for solar power satellites,and ambient microwave wireless energy harvesting(MWEH)in urban areas,few studies focus on the fundamental modeling and related design automation of receiver systems.This article reviews the development of MWPT and MWEH receivers,with a focus on rectenna design automation.A novel rectifier model capable of accurately modeling the rectification process under both high and low input power is presented.The model reveals the theoretical boundary of radio frequency-to-direct current(dc)power conversion efficiency and,most importantly,enables an automated system design.The automated rectenna design flow is sequential,with the minimal engagement of iterative optimization.It covers the design automation of every module(i.e.,rectifiers,matching circuits,antennae,and dc–dc converters).Scaling-up of the technique to large rectenna arrays is also possible,where the challenges in array partitioning and power combining are briefly discussed.In addition,several cutting-edge rectenna techniques for MWPT and MWEH are reviewed,including the dynamic range extension technique,the harmonics-based retro-directive technique,and the simultaneous wireless information and power transfer technique,which can be good complements to the presented automated design methodology.

Dopaminergic Neurons in the Ventral Tegmental–Prelimbic Pathway Promote the Emergence of Rats from Sevoflurane Anesthesia

Dopaminergic neurons in the ventral tegmental area(VTA)play an important role in cognition,emergence from anesthesia,reward,and aversion,and their projection to the cortex is a crucial part of the"bottom-up"ascending activating system.The prelimbic cortex(PrL)is one of the important projection regions of the VTA.However,the roles of dopaminergic neurons in the VTA and the VTADA–PrL pathway under sevoflurane anesthesia in rats remain unclear.In this study,we found that intraperitoneal injection and local microinjection of a dopamine D1 receptor agonist(Chloro-APB)into the PrL had an emergence-promoting effect on sevoflurane anesthesia in rats,while injection of a dopamine D1 receptor antagonist(SCH23390)deepened anesthesia.The results of chemogenetics combined with microinjection and optogenetics showed that activating the VTADA–PrL pathway prolonged the induction time and shortened the emergence time of anesthesia.These results demonstrate that the dopaminergic system in the VTA has an emergence-promoting effect and that the bottom-up VTADA–PrL pathway facilitates emergence from sevoflurane anesthesia.

Integrated representation of geospatial data,model,and knowledge for digital twin railway

The real-time accurate description of all spatial features of railway and their spatiotemporal relationships is a crucial factor in realizing comprehensive management and related decision-making within the entire life cycle of railways.Nevertheless,available spatiotemporal data models mainly use static historical sequence data,which are insufficient to support multi-source heterogeneous real-time sensed data;they lack a systematic depiction of the interactive relationships among multiple feature entities,and are limited to low-level descriptive analysis.Therefore,this study proposes a data-model-knowledge integrated representation data model for a digital twin railway,which explicitly describes the spatiotemporal,and interaction relationships among railway features through a conceptual knowledge graph.This study first analyzes the characteristics of railway features from above ground to underground,and then constructs a conceptual model to clearly describe the complex relationships among railway features.Secondly,a logical model is developed to illustrate the basic data structure.Thirdly,an ontology model is constructed as a basic framework for further deepening the domain knowledge graph.Finally,considering the prevention of landslides as an example,it demonstrates the abundant spatiotemporal relationships among railway related features.The results of this study bring more clear understanding of the complex interactive relationships of railway entities.

An adaptive LIC based geographic flow field visualization method by means of rotation distance

Geographic visualization is essential for explaining and describing spatiotemporal geographical processes in flow fields.However,due to multi-scale structures and irregular spatial distribution of vortices in complex geographic flow fields,existing two-dimensional visualization methods are susceptible to the effects of data accuracy and sampling resolution,resulting in incomplete and inaccurate vortex information.To address this,we propose an adaptive Line Integral Convolution(LIC)based geographic flow field visualization method by means of rotation distance.Our novel framework of rotation distance and its quantification allows for the effective identification and extraction of vortex features in flow fields effectively.We then improve the LIC algorithm using rotation distance by constructing high-frequency noise from it as input to the convolution,with the integration step size adjusted.This approach allows us to effectively distinguish between vortex and non-vortex fields and adaptively represent the details of vortex features in complex geographic flow fields.Our experimental results show that the proposed method leads to more accurate and effective visualization of the geographic flow fields.

On large-signal modeling of GaN HEMTs: past, development and future

In the past few decades,circuits based on gallium nitride high elec-tron mobility transistor(GaN HEMT)have demonstrated exceptional potential in a wide range of high-power and high-frequency applica-tions,such as the new generation mobile communications,object de-tection and consumer electronics,etc.As a critical intermediary be-tween GaN HEMT devices and circuit-level applications,GaN HEMT large-signal models play a pivotal role in the design,application and development of GaN HEMT devices and circuits.This review pro-vides an in-depth examination of the advancements in GaN HEMT large-signal modeling in recent decades.Detailed and comprehensive coverage of various aspects of GaN HEMT large-signal model was of-fered,including large-signal measurement setups,classical formula-tion methods,model classification and non-ideal effects,etc.In order to better serve follow-up researches,this review also explored poten-tial future directions for the development of GaN HEMT large-signal modeling.