Ultrahigh performance passive radiative cooling by hybrid polar dielectric metasurface thermal emitters

Real-world passive radiative cooling requires highly emissive,selective,and omnidirectional thermal emitters to maintain the radiative cooler at a certain temperature below the ambient temperature while maximizing the net cooling power.Despite various selective thermal emitters have been demonstrated,it is still challenging to achieve these conditions sim-ultaneously because of the extreme difficulty in controlling thermal emission of photonic structures in multidimension.Here we demonstrated hybrid polar dielectric metasurface thermal emitters with machine learning inverse design,en-abling a high emissivity of~0.92 within the atmospheric transparency window 8-13μm,a large spectral selectivity of~1.8 and a wide emission angle up to 80 degrees,simultaneously.This selective and omnidirectional thermal emitter has led to a new record of temperature reduction as large as~15.4°C under strong solar irradiation of~800 W/m2,signific-antly surpassing the state-of-the-art results.The designed structures also show great potential in tackling the urban heat island effect,with modelling results suggesting a large energy saving and deployment area reduction.This research will make significant impact on passive radiative cooling,thermal energy photonics and tackling global climate change.

RFFsNet-SEI:a multidimensional balanced-RFFs deep neural network framework for specific emitter identification

Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.

Phonon-assisted upconversion photoluminescence of quantum emitters

Quantum emitters are widely used in quantum networks,quantum information processing,and quantum sensing due to their excellent optical properties.Compared with Stokes excitation,quantum emitters under anti-Stokes excitation exhibit better performance.In addition to laser cooling and nanoscale thermometry,anti-Stokes excitation can improve the coherence of single-photon sources for advanced quantum technologies.In this review,we follow the recent advances in phononassisted upconversion photoluminescence of quantum emitters and discuss the upconversion mechanisms,applications,and prospects for quantum emitters with anti-Stokes excitation.

Emitter discharge characteristics of vertical tube irrigation affected by various factors

To examine the working principle of vertical tube irrigation, variations in vertical tube emitter discharge and their causes were analyzed in the laboratory experiment. The effects of the pressure head, initial soil water content, and tube diameter on the emitter discharge of the vertical tube were studied. The results show that quantitative relationship between the time and cumulative infiltration and emitter discharge of the vertical tube is obtained, and R 2 is more than 0.98. Emitter discharge exhibits a positive and negative correlation with the pressure head and soil water content, respectively. Tube dia- meter has a nonsignificant effect on the emitter discharge. Changes of the soil water content around the emitter water outlet are the main causes of emitter discharge variations. In the experiments, the range of vertical tube emitter discharge is 0.056-1.102 L/h. The emitter of vertical tube irrigation automatically adjusts the soil water content and maintains the root zone soil water content within an appropriate range, which achieves continuous irrigation, and further achieves the effect of water-saving.

Radar emitter signal recognition method based on improved collaborative semi-supervised learning

Rare labeled data are difficult to recognize by using conventional methods in the process of radar emitter recogni-tion.To solve this problem,an optimized cooperative semi-supervised learning radar emitter recognition method based on a small amount of labeled data is developed.First,a small amount of labeled data are randomly sampled by using the bootstrap method,loss functions for three common deep learning net-works are improved,the uniform distribution and cross-entropy function are combined to reduce the overconfidence of softmax classification.Subsequently,the dataset obtained after sam-pling is adopted to train three improved networks so as to build the initial model.In addition,the unlabeled data are preliminarily screened through dynamic time warping(DTW)and then input into the initial model trained previously for judgment.If the judg-ment results of two or more networks are consistent,the unla-beled data are labeled and put into the labeled data set.Lastly,the three network models are input into the labeled dataset for training,and the final model is built.As revealed by the simula-tion results,the semi-supervised learning method adopted in this paper is capable of exploiting a small amount of labeled data and basically achieving the accuracy of labeled data recognition.

Super Performance InGaP/GaAs Heterojunction Bipolar Transistor with Hexagonal Emitter

Super performance InGaP/GaAs heterojunction bipolar transistors (HBTs) with hexagonal emitter are described.The fabricated HBT shows excellent DC characteristics with low V CE offset voltage (<0 15V) and low knee voltage (<0 5V).Over 14V of the collector base breakdown voltage BV CBO and over 9V of the collector emitter breakdown voltage BV CEO are obtained.For a self aligned InGaP/GaAs HBT with 2μm×10μm emitter area,the f T is extrapolated to 92GHz and f max is extrapolated to 105GHz.These great values are obtained due to the hexagonal emitter and laterally etched undercut (LEU) of collector,indicating the great potential of InGaP/GaAs HBTs for high speed digital circuit and microwave power applications.

Degradation of the Emission Current from the Field Emitter Caused by Ion Bombardment

In field emission devices, the emission current sometimes degrades with the time. The mechanism of the current degradation is complicated. In this paper, a program is used to simulate the movement of the electron beam from a field emitter. According to the current distribution and the trajectories of the primary electron beam, it is shown that the residual gas is ionized and the ion pairs are generated. The trajectories of the positive ions are simulated. With the different locations and kinetic energy of i...

Tomato Yield and Quality and Emitter Clogging as Affected by Chlorination Schemes of Drip Irrigation Systems Applying Sewage Effluent

Chlorination has been recognized as an efficient and economically favorable method for treating clogging in drip emitters caused by biological growth during sewage application. Further important criteria for determining an optimal chlorination scheme are the different responses of crops to the chloride added into the soil through chlorination. During two seasons in 2008 and 2009, field experiments were conducted in a solar-heated greenhouse with drip irrigation systems applying secondary sewage effluent to tomato plants to investigate the influences of chlorine injection intervals and levels on plant growth, yield, fruit quality, and emitter clogging. Injection intervals ranging from 2 to 8 wk and injection concentrations ranging 2-50 mg L-1 of free chlorine residual at the end of the laterals were used. For the 2008 experiments, the yield from the treatments of sewage application with chlorination was 7.5% lower than the yield from the treatment of sewage application without chlorination, while the yields for the treatments with and without chlorination were similar for the 2009 experiments. The statistical tests indicated that neither the chlorine injection intervals and concentrations nor the interactions between the two significantly influenced plant height, leaf area, or tomato yield for both years. The qualities of the fruit in response to chlorination were parameter-dependent. Chlorination did not significantly influence the quality of ascorbic acid, soluble sugar, or soluble acids, but the interaction between the chlorine injection interval and the chlorine concentration significantly influenced the levels of soluble solids. It was also confirmed that chlorination was an effective method for reducing biological clogging. These results suggested that chlorination is safe for a crop that has a moderate sensitivity to chlorine, like tomato, and can maintain a high level of performance in drip irrigation systems applying sewage effluent.

Performance of a Self-Aligned InP/GaInAs SHBT with a Novel T-Shaped Emitter

A self-aligned InP/GalnAs single heterojunction bipolar transistor（HBT） is investigated using a novel T-shaped emitter. A U-shaped emitter layout,selective wet etching,laterally etched undercut, and an air-bridge are applied in this process. The device, which has a 2μm×12μm U-shaped emitter area,demonstrates a common-emitter DC current gain of 170,an offset voltage of 0.2V,a knee voltage of 0.5V, and an open-base breakdown voltage of over 2V. The HBT exhibits good microwave performance with a current gain cutoff frequency of 85GHz and a maximum oscillation frequency of 72GHz, These results indicate that these InP/InGaAs SHBTs are suitable for low-voltage,low-power,and high-frequency applications.

Flow Behaviour Analysis and Experimental Investigation for Emitter Micro-channels

The existing research of the flow behavior in emitter micro-channels mainly focuses on the single-phase flow behavior.And the recent micro-particle image velocimetry（PIV） experimental research on the flow characteristics in various micro-channels mainly focuses on the single-phase fluid flow.However,using an original-size emitter prototype to perform the experiments on the two-phase flow characteristics of the labyrinth channels is seldom reported.In this paper,the practical flow of water,mixed with sand escaped from filtering,in the labyrinth channel,is investigated.And some research work on the clogging mechanism of the labyrinth channel＇s structure is conducted.Computational fluid dynamics（CFD） analysis has been performed on liquid-solid two-phase flow in labyrinth-channel emitters.Based on flow visualization technology-micro-PIV,the flow in labyrinth channel has been photographed and recorded.The path line graph and velocity vector graph are obtained through the post-treatment of experimental results.The graphs agree well with CFD analysis results,so CFD analysis can be used in optimal design of labyrinth-channel emitters.And the optimized anti-clogging structures of the rectangular channel and zigzag channel have been designed here.The CFD numerical simulation and the micro-PIV experiments analysis on labyrinth-channel emitter,make the ＂black box＂ of the flow behavior in the emitter channel broken.Furthermore,the proposed research promotes an advanced method to evaluate the emitter＇s performance and can be used to conducting the optimal design of the labyrinth-channel emitters.

Effects of emitter discharge rates on soil salinity distribution and cotton(Gossypium hirsutum L.) yield under drip irrigation with plastic mulch in an arid region of Northwest China

A field experiment was carried out to investigate the effects of different emitter discharge rates under drip irrigation on soil salinity distribution and cotton yield in an extreme arid region of Tarim River catchment in Northwest China. Four treatments of emitter discharge rates, i.e. 1.8, 2.2, 2.6 and 3.2 L/h, were designed under drip irrigation with plastic mulch in this paper. The salt distribution in the range of 70-cm horizontal distance and 100-cm vertical distance from the emitter was measured and analyzed during the cotton growing season. The soil salinity is expressed in terms of electrical conductivity （dS/m） of the saturated soil extract （ECe）, which was measured using Time Domain Reflector （TDR） 20 times a year, including 5 irrigation events and 4 measured times before/after an irrigation event. All the treatments were repeated 3 times. The groundwater depth was observed by SEBA MDS Dipper 3 automatically at three experimental sites. The results showed that the order of reduction in averaged soil salinity was 2.6 L/h 〉 2.2 L/h 〉 1.8 L/h 〉 3.2 L/h after the completion of irrigation for the 3-year cotton growing season. Therefore, the choice of emitter discharge rate is considerably important in arid silt loam. Usually, the ideal emitter discharge rate is 2.4-3.0 L/h for soil desalinization with plastic mulch, which is advisable mainly because of the favorable salt leaching of silt loam and the climatic conditions in the studied arid area. Maximum cotton yield was achieved at the emitter discharge rate of 2.6 L/h under drip irrigation with plastic mulch in silty soil at the study site. Hence, the emitter discharge rate of 2.6 L/h is recommended for drip irrigation with plastiic mulch applied in silty soil in arid regions.

Radar Emitter Signal Recognition Based on Complexity Features

Intra-pulse characteristics of different radar emitter signals reflect on signal waveform by way of changing frequency, phase and amplitude. A novel approach was proposed to extract complexity features of radar emitter signals in a wide range of signal-to-noise ratio (SNR), and radial basis probability neural network (RBPNN) was used to recognize different radar emitter signals. Complexity features, including Lempel-Ziv complexity (LZC) and correlation dimension (CD), can measure the complexity and irregularity of signals, which mirrors the intra-pulse modulation laws of radar emitter signals. In an experiment, LZC and CD features of 10 typical radar emitter signals were extracted and RBPNN was applied to identify the 10 radar emitter signals. Simulation results show that the proposed approach is effective and has good application values because average accurate recognition rate is high when SNR varies in a wide range.

Biofilm structure and its influence on clogging in drip irrigation emitters distributing reclaimed wastewater

Using reclaimed wastewater for crop irrigation is a practical alternative to discharge wastewater treatment plant effluents into surface waters.However,biofouling has been identified as a major contributor to emitter clogging in drip irrigation systems distributing reclaimed wastewater.Little is known about the biofilm structure and its influence on clogging in the drip emitter flow path.This study was first to investigate the microbial characteristics of mature biofilms present in the emitters and the effect of flow path structures on the biofilm microbial communities.The analysis of biofilm matrix structure using a scanning electron microscopy（SEM） revealed that particles in the matrix of the biofilm coupled extracellular polysaccharides（EPS） and formed sediment in the emitter flow path.Analysis of biofilm mass including protein,polysaccharide,and phospholipid fatty acids（PLFAs） showed that emitter flow path style influenced biofilm community structure and diversity.The correlations of biofilm biomass and discharge reduction after 360 h irrigation were computed and suggest that PFLAs provide the best correlation coeffcient.Comparatively,the emitter with the unsymmetrical dentate structure and shorter flow path（Emitter C） had the best anti-clogging capability.By optimizing the dentate structure,the internal flow pattern within the flow path could be enhanced as an important method to control the biofilm within emitter flow path.This study established electron microscope techniques and biochemical microbial analysis methods that may provide a framework for future emitter biofilm studies.

Optimization of Metal Coverage on the Emitter in n-Type Interdigitated Back Contact Solar Cells Using a PC2D Simulation

In interdigitated back contact(IBC)solar cells,the metal-electrode coverage on a p-type emitter is optimized by a PC2D simulation.The result shows that the variation of the metal coverage ratio(MCR)will affect both the surface passivation and the electrode-contact properties for the p-type emitter in IBC solar cells.We find that when R_(c) ranges from 0.08 to 0.16Ω·cm^(2),the MCR is optimized with a value of 25%and 33%,resulting in a highest energy-conversion efficiency.The dependences of both Voc and fill factor on MCR are simulated in order to explore the mechanism of the IBC solar cells.

Radar Emitter Signal Recognition Using Wavelet Packet Transform and Support Vector Machines

This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform （WPT） is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines （SVMs） are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.

Radar emitter multi-label recognition based on residual network

In low signal-to-noise ratio(SNR)environments,the traditional radar emitter recognition(RER)method struggles to recognize multiple radar emitter signals in parallel.This paper proposes a multi-label classification and recognition method for multiple radar-emitter modulation types based on a residual network.This method can quickly perform parallel classification and recognition of multi-modulation radar time-domain aliasing signals under low SNRs.First,we perform time-frequency analysis on the received signal to extract the normalized time-frequency image through the short-time Fourier transform(STFT).The time-frequency distribution image is then denoised using a deep normalized convolutional neural network(DNCNN).Secondly,the multi-label classification and recognition model for multi-modulation radar emitter time-domain aliasing signals is established,and learning the characteristics of radar signal time-frequency distribution image dataset to achieve the purpose of training model.Finally,time-frequency image is recognized and classified through the model,thus completing the automatic classification and recognition of the time-domain aliasing signal.Simulation results show that the proposed method can classify and recognize radar emitter signals of different modulation types in parallel under low SNRs.

Effect of drip irrigation method, nitrogen source, and flushing schedule on emitter clogging

Field experiments were carried out at the National Research Center farm, Nubaria area, Behura Governorate, Egypt, to study the effect of nitrogen source, flushing schedule and irrigation method on emitter clogging. Peanut Giza 5 variety (Arachishy pogaea L.) was planted in sandy soil during two successive growing seasons (2010-2011) in the 1st week of May and harvested after 130 days. Treatments used are: 1) two irrigation methods: surface drip irrigation and sub-surface drip irrigation (SDI;SSDI), 2) nitrogen source (NS):NH4NO3, (NH4)2SO4 and Ca(NO3)2 (NS1, NS2 and NS3) and 3) flushing number (FL) 0, 1 and 4 (FL1, FL2;FL3). The experiments design was split-split plot and three replicates were used. Data obtained were subjected to statistical analysis. The main effects of treatments used on clogging per cent could be written the following ascending orders: SDI 3 2 1, NS1 2 3. Concerning the 1st interaction the following ascending orders denote their effects on clogging percent: SDI × FL3 2 1, SDI × NS1 2 3, SSDI × FL3 2 1, SSDI × NS1 2 3, FL1 × NS1 1 × NS2 1 × NS3, FL2 × NS1 2 × NS2 2 × NS3 and FL3 × NS1 3 × NS2 3 × NS3. The differences between any two treatments and/or any two interactions in clogging percent were significant at the 5% level. The effect of the 2nd interaction on clogging percent was significant at the 5% level. The maximum value of clogging (20.18%) and the lowest one (3.9%) were archived in the interactions: SSDI × FL1 × NS3 and SDI × FL3 × NS1, respectively.

Comparing and evaluating the nationally determined contributions of the top six emitters under the Paris Agreement goals

Comparing and evaluating the Nationally Determined Contribution(NDC) is an important element in global stocktake in the post-Paris climate negotiations, aimed at closing the emissions gap with the Paris Agreement goals. To date, however, there has still been no explicit guideline or method. By applying emissions allowance allocated by 16 schemes as benchmarks, this paper tries to compare and evaluate the NDCs of the top six emitters, which jointly account for about 70% of the world's CO_2 emissions. Results show that the four developed countries' NDCs lack ambition with respect to most allocations under 2℃ and all under 1.5℃, indicating they need to substantially ratchet up their NDCs and lead elevating mitigation. Evaluating cumulative emissions is more likely to clarify the ambition and fairness of China's NDC. If considering cumulative emissions, China's NDC is aligned with the median of cumulative allowances under 2℃ and within the 1.5℃ range. The Paris Agreement invited the Parties to communicate the mid-century low emissions strategies. This paper also tries to explore the mid-century mitigation in the perspective of allocations, which might provide decision-makers with some useful information when envisaging the post-NDC mitigation.

Effects of arrangement of surge-root irrigation emitters on growth,yield and water use efficiency of apple trees

Six-year old apple trees were selected for field experiment.The objective of this study was to obtain the reasonable arrangement of surge-root irrigation emitters in apple orchards.There were three factors:the buried depth H(25,40,55 cm),the horizontal distance L(30,40,60 cm)between the emitters and the trunk of the experimental tree,and the number of the irrigation emitters N(1,2,4).The effect of the arrangement of surge-root irrigation emitters on the growth,yield and irrigation water use efficiency(IWUE)of apple trees were studied in Northern Shaanxi where the irrigation quota takes 60%-75%of the field water capacity.The results showed that the arrangement of emitters for surge-root irrigation had a significant effect on apple tree yield and IWUE,especially,the yield and IWUE reached 28388.17 kg/hm2 and 16.83 kg/m3 in treatment T3,respectively.At the same L and N levels(T1,T2,and T3),the yield and IWUE in treatment T3 were the highest,and the yields in treatments T1 and T2 were decreased by 26.22%and 31.48%,while IWUE is reduced by14.02%and 18.12%compared with T3,respectively.At the same H and N levels(T3,T4,and T5),the yield and IWUE of apple trees were decreased with increasing L level.Especially,when L was 30 cm(T3),the yield and IWUE were the highest.The same L and H levels(T3,T6,and T7)could promote the growth of apple trees when N was 2(T3).Compared with treatment T3,it was found that the increment of new shoots was decreased by 8.07%-18.71%,and the fruit diameter was decreased by 5.41%-9.11%.Therefore,two emitters should be arranged symmetrically on both sides of an apple tree,each was buried at a 40 cm depth and 30 cm away from the trunk of the tree to effectively improve the yield and IWUE of the apple tree in mountainous areas in Northern Shaanxi.

Specific Emitter Identification for IoT Devices Based on Deep Residual Shrinkage Networks

Specific emitter identification can distin-guish individual transmitters by analyzing received signals and extracting inherent features of hard-ware circuits.Feature extraction is a key part of traditional machine learning-based methods,but manual extrac-tion is generally limited by prior professional knowl-edge.At the same time,it has been noted that the per-formance of most specific emitter identification meth-ods degrades in the low signal-to-noise ratio(SNR)environments.The deep residual shrinkage network(DRSN)is proposed for specific emitter identification,particularly in the low SNRs.The soft threshold can preserve more key features for the improvement of performance,and an identity shortcut can speed up the training process.We collect signals via the receiver to create a dataset in the actual environments.The DRSN is trained to automatically extract features and imple-ment the classification of transmitters.Experimental results show that DRSN obtains the best accuracy un-der different SNRs and has less running time,which demonstrates the effectiveness of DRSN in identify-ing specific emitters.