Intersubband transition in ZnO/MgZnO quantum well has been exploited for infrared and terahertz optoelectronic applications due to its large band offset and fascinating material properties.Here,we theoretically demons...Intersubband transition in ZnO/MgZnO quantum well has been exploited for infrared and terahertz optoelectronic applications due to its large band offset and fascinating material properties.Here,we theoretically demonstrate piezophototronic effect as another way to control the intersubband absorption wavelength through quantum-confined Stark effect.The intersubband optical absorption properties under different stresses are obtained by solving the eight-band k·p Hamiltonian and coupled Schr¨odinger-Poisson equations self-consistently.By combining stress control and quantum well structure,the absorption wavelength can show infrared blueshift or redshift phenomena in a wide range.This work can provide an effective avenue to control and utilize quantum-confined Stark effect in intersubband infrared absorption and promote the relative potential optoelectronic devices.展开更多
The development of deep learning has revolutionized image recognition technology.How to design faster and more accurate image classification algorithms has become our research interests.In this paper,we propose a new ...The development of deep learning has revolutionized image recognition technology.How to design faster and more accurate image classification algorithms has become our research interests.In this paper,we propose a new algorithm called stochastic depth networks with deep energy model(SADIE),and the model improves stochastic depth neural network with deep energy model to provide attributes of images and analysis their characteristics.First,the Bernoulli distribution probability is used to select the current layer of the neural network to prevent gradient dispersion during training.Then in the backpropagation process,the energy function is designed to optimize the target loss function of the neural network.We also explored the possibility of using Adam and SGD combination optimization in deep neural networks.Finally,we use training data to train our network based on deep energy model and testing data to verify the performance of the model.The results we finally obtained in this research include the Classified labels of images.The impacts of our obtained results show that our model has high accuracy and performance.展开更多
The microbial potential of Penicillium has received critical attention.The present research aimed to elucidate the efficacy of crude enzyme secreted from Penicillium oxalicum WX-209 in degrading citrus segments and ev...The microbial potential of Penicillium has received critical attention.The present research aimed to elucidate the efficacy of crude enzyme secreted from Penicillium oxalicum WX-209 in degrading citrus segments and evaluate the safety of the process.Results showed that citrus segment membranes gradually dissolved after treatment with the crude enzyme solution,indicating good degradation capability.No significant differences in body weight,food ingestion rate,hematology,blood biochemistry,and weight changes of different organs were found between the enzyme intake and control groups.Serial experiments showed that the crude enzyme had high biological safety.Moreover,the whole genome of P.oxalicum WX-209 was sequenced by PacBio and Illumina platforms.Twenty-five scaffolds were assembled to generate 36 Mbp size of genome sequence comprising 11369 predicted genes modeled with a GC content of 48.33%.A total of 592 genes were annotated to encode enzymes related to carbohydrates,and some degradation enzyme genes were identified in strain P.oxalicum WX-209.展开更多
The rapid growth of new electromechanical applications has increased the demand for ferroelectric ce-ramics with excellent piezoelectric properties and a wide temperature operating range.However,achiev-ing robust piez...The rapid growth of new electromechanical applications has increased the demand for ferroelectric ce-ramics with excellent piezoelectric properties and a wide temperature operating range.However,achiev-ing robust piezoelectricity and temperature stability simultaneously in lead zirconate titanate(Pb(Zr,Ti)O_(3))based piezoelectric ceramics poses a significant challenge.This study proposes a new material system of 0.98Pb_(0.84)Ba_(0.16)(Zr_(2/3)Ti_(1/3))O_(3)-0.02Pb(Sb_(1/2)Nb_(1/2))O_(3)+x wt.%Sm_(2)O_(3)(PBZT-PSN-x Sm)to address this challenge.Remarkably,the ceramics x=0.4 demonstrate excellent piezoelectric properties(includ-ing piezoelectric coefficient d33 of 610 pC/N and Curie temperature TC of 290℃)and favorable tem-perature stability(i.e.,d33 varies less than 10%within 25-200℃).The high piezoelectric properties arise from the optimized phase fraction between rhombohedral(R)and tetragonal(T)phases and the increased grain size,which enhance the lattice distortion and the domain switching under electric fields,respectively.The superior temperature stability can be attributed to stable crystal structure and domain structure.These findings indicate that PBZT-PSN-x Sm ceramics hold great promise for practical utilization in high-temperature transducers and sensors.展开更多
Maintenance is an important aspect in the lifecycle of communication network devices.Prevalent problems in the maintenance of communication networks include inconvenient data carrying and sub-optimal scheduling of wor...Maintenance is an important aspect in the lifecycle of communication network devices.Prevalent problems in the maintenance of communication networks include inconvenient data carrying and sub-optimal scheduling of work orders,which significantly restrict the efficiency of maintenance work.Moreover,most maintenance systems are still based on cloud architectures that slow down data transfer.With a focus on the completion time,quality,and load balancing of maintenance work,we propose in this paper a learning-based virus evolutionary genetic algorithm with multiple quality-ofservice(QoS)constraints to implement intelligent scheduling in an edge network.The algorithm maintains the diversity of the population and improves the speed of convergence using a fitness function and a learning-based population generation mechanism.The test results demonstrate that the algorithm delivers good performance in terms of load balancing and QoS guarantee.We also propose a knowledge push algorithm based on a context model for intelligently pushing relevant knowledge according to the given conditions.The simulation results demonstrate that our scheme can improve the efficiency of on-site maintenance.展开更多
It has been known that metal FeSiCr powders with large average particle sizes have been typically employed to prepare magnetic powder cores(SMCs),with few studies reported on the influence of magnetic properties for o...It has been known that metal FeSiCr powders with large average particle sizes have been typically employed to prepare magnetic powder cores(SMCs),with few studies reported on the influence of magnetic properties for original powders with various average particle sizes less than 10m.In this work,SiO_(2)-coated FeSiCr SMCs with different small particle sizes were synthesized using the sol-gel process.The contribution of SiO_(2)coating amount and voids to the soft magnetic properties was elaborated.The mechanism was revealed such that smaller particle sizes with less voids could be beneficial for reducing core loss in the SMCs.By optimizing the core structure,permeability and magnetic loss of 26 and 262 kW/cm^(3)at 100 kHz and 50 mT were achieved at a particle size of 4.8m and ethyl orthosilicate addition of 0.1 mL/g.The best DC stacking performance,reaching 87%,was observed at an ethyl orthosilicate addition rate of 0.25 mL/g under 100 Oe.Compared to other soft magnetic composites(SMCs),the FeSiCr/SiO_(2)SMCs exhibit significantly reduced magnetic loss.It further reduces the magnetic loss of the powder core,providing a new strategy for applications of SMCs at high frequencies.展开更多
Arterial characterization of healthy descending thoracic aorta(DTA)is indispensable in determining stress distributions across wall thickness and different regions that may be responsible for aorta inhomogeneous dilat...Arterial characterization of healthy descending thoracic aorta(DTA)is indispensable in determining stress distributions across wall thickness and different regions that may be responsible for aorta inhomogeneous dilation,rupture,and dissection when aneurysm occurs.Few studies have shown the inhomogeneity of DTA along the aorta tree considering changes in circumferential direction.The present study aims to clarify the circumferential regional characterization of DTA.Porcine DTA tissues were tested according to region and orientation using uniaxial tension.For axial test,results show that the difference in circumferential direction was mainly in collagen fiber modulus,where the anterior collagen fiber modulus was significantly lower than the posterior quadrant.For circumferential test,the difference in circumferential direction was mainly in the recruitment parameter,where the circumferential stiffness is significantly higher in the posterior region at physiological maximum stress.The proximal posterior quadrant and left quadrant showed significantly lower axial collagen fiber stiffness than the right and anterior quadrants,which may be a factor in aneurysm development.Furthermore,the constitutive parameters for similar detailed regions can be used by biomedical engineers to investigate improved therapies and thoroughly understand the initial stage of aneurysm development.The regional collagen fiber modulus can help improve our understanding of the mechanisms of arterial dilation and aortic dissection.展开更多
Manipulating metal-insulator transitions in strongly correlated materials is of great importance in condensed matter physics,with implications for both fundamental science and technology.Vanadium dioxide(VO_(2)),as an...Manipulating metal-insulator transitions in strongly correlated materials is of great importance in condensed matter physics,with implications for both fundamental science and technology.Vanadium dioxide(VO_(2)),as an ideal model system,is metallic at high temperatures and shown a typical metal-insulator structural phase transition at 341 K from rutile structure to monoclinic structure.This behavior has been absorbed tons of attention for years.However,how to control this phase transition is still challenging and little studied.Here we demonstrated that to control the Ag nanonet arrays(NAs)in monoclinic VO_(2)(M)could be effective to adjust this metal-insulator transition.With the increase of Ag NAs volume fraction by reducing the template spheres size,the transition temperature(Tc)decreased from 68°C to 51°C.The mechanism of Tc decrease was revealed as:the carrier density increases through the increase of Ag NAs volume fraction,and more free electrons injected into the VO_(2)films induced greater absorption energy at the internal nanometal-semiconductor junction.These results supply a new strategy to control the metal-insulator transitions in VO_(2),which must be instructive for the other strongly correlated materials and important for applications.展开更多
Size and morphology are critical factors in determining the electrochemical performance of the supercapacitor materials,due to the manifestation of the nanosize effect.Herein,different nanostructures of the CrN materi...Size and morphology are critical factors in determining the electrochemical performance of the supercapacitor materials,due to the manifestation of the nanosize effect.Herein,different nanostructures of the CrN material are prepared by the combination of a thermal-nitridation process and a template technique.High-temperature nitridation could not only transform the hexagonal Cr_(2)O_(3)into cubic CrN,but also keep the template morphology barely unchanged.The obtained CrN nanostructures,including(i)hierarchical microspheres assembled by nanoparticles,(ii)microlayers,and(iii)nanoparticles,are studied for the electrochemical supercapacitor.The CrN microspheres show the best specific capacitance(213.2 F/g),cyclic stability(capacitance retention rate of 96%after 5000 cycles in 1-mol/L KOH solution),high energy density(28.9 Wh/kg),and power density(443.4 W/kg),comparing with the other two nanostructures.Based on the impedance spectroscopy and nitrogen adsorption analysis,it is revealed that the enhancement arised mainly from a high-conductance and specific surface area of CrN microspheres.This work presents a general strategy of fabricating controllable CrN nanostructures to achieve the enhanced supercapacitor performance.展开更多
Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering the unique characteristics ...Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering the unique characteristics of edge networks,such as limited resources,complex network faults,and the need for high real-time performance,enhancing and optimizing existing network fault diagnosis methods is necessary.Therefore,this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network(LSNN).Firstly,we use the Izhikevich neurons model to replace the Leaky Integrate and Fire(LIF)neurons model in the LSNN model.Izhikevich neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data inputs.Inspired by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of FSIs.Secondly,inspired by the connection mode based on spiking dynamics in the basal ganglia(BG)area of the brain,we propose the pruning approach based on the FSIs of the BG in LSNN to improve computational efficiency and reduce the demand for computing resources and energy consumption.Furthermore,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN model.Experiments on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis dimensions.Simultaneously,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model efficiency.To conclude,experiment results on a pair of datasets indicate that the LSNN model surpasses traditional models and achieves cutting-edge outcomes in network fault diagnosis tasks.展开更多
Nowadays,ensuring thequality of networkserviceshas become increasingly vital.Experts are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device configurat...Nowadays,ensuring thequality of networkserviceshas become increasingly vital.Experts are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device configurations.This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms.Initially,an improved active learning approach is employed to select the most valuable unlabeled samples,which are subsequently submitted for expert labeling.This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set.Then the labeled samples are utilized to train the model for network configuration entity extraction.Furthermore,the multi-head self-attention of the transformer model is enhanced by introducing the Adaptive Weighting method based on the Laplace mixture distribution.This enhancement enables the transformer model to dynamically adapt its focus to words in various positions,displaying exceptional adaptability to abnormal data and further elevating the accuracy of the proposed model.Through comparisons with Random Sampling(RANDOM),Maximum Normalized Log-Probability(MNLP),Least Confidence(LC),Token Entrop(TE),and Entropy Query by Bagging(EQB),the proposed method,Entropy Query by Bagging and Maximum Influence Active Learning(EQBMIAL),achieves comparable performance with only 40% of the samples on both datasets,while other algorithms require 50% of the samples.Furthermore,the entity extraction algorithm with the Adaptive Weighted Multi-head Attention mechanism(AW-MHA)is compared with BILSTM-CRF,Mutil_Attention-Bilstm-Crf,Deep_Neural_Model_NER and BERT_Transformer,achieving precision rates of 75.98% and 98.32% on the two datasets,respectively.Statistical tests demonstrate the statistical significance and effectiveness of the proposed algorithms in this paper.展开更多
With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant trend.The existing fault diagnosismethods have the characteristics of heavy computing and st...With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant trend.The existing fault diagnosismethods have the characteristics of heavy computing and storage load,and most of them have computational redundancy,which is not suitable for deployment on edge devices with limited resources and capabilities.This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation.First,we offer a clustering-based self-knowledge distillation approach(Cluster KD),which takes the mean value of the sample diagnosis results,clusters them,and takes the clustering results as the terms of the loss function.It utilizes the correlations between faults of the same type to improve the accuracy of the teacher model,especially for fault categories with high similarity.Then,the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweightmodel for edge-side deployment.We propose a two-stage edge-side fault diagnosismethod(TSM)that separates fault detection and fault diagnosis into different stages:in the first stage,a fault detection model based on a denoising auto-encoder(DAE)is adopted to achieve fast fault responses;in the second stage,a diverse convolutionmodel with variance weighting(DCMVW)is used to diagnose faults in detail,extracting features frommicro andmacro perspectives.Through comparison experiments conducted on two fault datasets,it is proven that the proposed method has high accuracy,low delays,and small computation,which is suitable for intelligent edge-side fault diagnosis.In addition,experiments show that our approach has a smooth training process and good balance.展开更多
The rapid growth of modern mobile devices leads to a large number of distributed data,which is extremely valuable for learning models.Unfortunately,model training by collecting all these original data to a centralized...The rapid growth of modern mobile devices leads to a large number of distributed data,which is extremely valuable for learning models.Unfortunately,model training by collecting all these original data to a centralized cloud server is not applicable due to data privacy and communication costs concerns,hindering artificial intelligence from empowering mobile devices.Moreover,these data are not identically and independently distributed(Non-IID)caused by their different context,which will deteriorate the performance of the model.To address these issues,we propose a novel Distributed Learning algorithm based on hierarchical clustering and Adaptive Dataset Condensation,named ADC-DL,which learns a shared model by collecting the synthetic samples generated on each device.To tackle the heterogeneity of data distribution,we propose an entropy topsis comprehensive tiering model for hierarchical clustering,which distinguishes clients in terms of their data characteristics.Subsequently,synthetic dummy samples are generated based on the hierarchical structure utilizing adaptive dataset condensation.The procedure of dataset condensation can be adjusted adaptively according to the tier of the client.Extensive experiments demonstrate that the performance of our ADC-DL is more outstanding in prediction accuracy and communication costs compared with existing algorithms.展开更多
Myxozoans are common microscopic endoparasites in fish,and some are highly pathogenic to their wild and farmed fish hosts.In the present study,myxosporeans infection in the kidney of silver carp Hypophthalmichthys mol...Myxozoans are common microscopic endoparasites in fish,and some are highly pathogenic to their wild and farmed fish hosts.In the present study,myxosporeans infection in the kidney of silver carp Hypophthalmichthys molitrix(Valenciennes,1844)from Lake Taihu,was investigated,and two dominate species,Myxobolus lieni(Nie&Li,1973)and M.varius(Achmerov,1960),with infection prevalence 60.2%and 35.2%,respectively,were well characterized based on morphological,histopathological and DNA sequence data.M.lieni formed small roundish plasmodia in the epithelial cells of renal tubules.The mature myxospores appeared suborbicular,slightly flat in frontal view and fusiform shaped in sutural view.Dispersed myxospores of M.varius were found in the renal interstitium without forming plasmodia structures and enclosed within melano-macrophage centers.The spore appeared elliptical in frontal view,with wider anterior than posterior and shuttle shaped in sutural view.Interestingly,the occurrence of myxozoans in the kidney detected with SSU rDNA PCR and clone sequencing,revealed co-infection of five Myxobolus species.BLASTn search indicated SSU r DNA gene sequences obtained here were not identical to any sequence available in GenBank.Phylogenetic analyses showed that the five Myxobolus species detected here were clustered together,forming a separate clade of cyprinid-infecting myxozoans.展开更多
Crowdsensing,as a data collection method that uses the mobile sensing ability of many users to help the public collect and extract useful information,has received extensive attention in data collection.Since crowdsens...Crowdsensing,as a data collection method that uses the mobile sensing ability of many users to help the public collect and extract useful information,has received extensive attention in data collection.Since crowdsensing relies on user equipment to consume resources to obtain information,and the quality and distribution of user equipment are uneven,crowdsensing has problems such as low participation enthusiasm of participants and low quality of collected data,which affects the widespread use of crowdsensing.This paper proposes to apply the blockchain to crowdsensing and solve the above challenges by utilizing the characteristics of the blockchain,such as immutability and openness.An architecture for constructing a crowdsensing incentive mechanism under distributed incentives is proposed.A multi-attribute auction algorithm and a k-nearest neighbor-based sensing data quality determination algorithm are proposed to support the architecture.Participating users upload data,determine data quality according to the algorithm,update user reputation,and realize the selection of perceived data.The process of screening data and updating reputation value is realized by smart contracts,which ensures that the information cannot be tampered with,thereby encouraging more users to participate.Results of the simulation show that using two algorithms can well reflect data quality and screen out malicious data.With the help of blockchain performance,the architecture and algorithm can achieve decentralized storage and tamper-proof information,which helps to motivate more users to participate in perception tasks and improve data quality.展开更多
Real-time Fraud Detection has always been a challenging task, especially in financial, insurance, and telecom industries. There are mainly three methods, which are rule set, outlier detection and classification to sol...Real-time Fraud Detection has always been a challenging task, especially in financial, insurance, and telecom industries. There are mainly three methods, which are rule set, outlier detection and classification to solve the problem. But those methods have some drawbacks respectively. To overcome these limitations, we propose a new algorithm UAF (Usage Amount Forecast).Firstly, Manhattan distance is used to measure the similarity between fraudulent instances and normal ones. Secondly, UAF gives real-time score which detects the fraud early and reduces as much economic loss as possible. Experiments on various real-world datasets demonstrate the high potential of UAF for processing real-time data and predicting fraudulent users.展开更多
On-Line Analytical Processing (OLAP) is based on pre-computation of data cubes, which greatly reduces the response time and improves the performance of OLAP. Frag-Shells algorithm is a common method of precomputation....On-Line Analytical Processing (OLAP) is based on pre-computation of data cubes, which greatly reduces the response time and improves the performance of OLAP. Frag-Shells algorithm is a common method of precomputation.However, it relies too much on the data dispersion that it performs poorly, when confronts large amount of highly disperse data. As the amount of data grows fast nowadays, the efficiency of data cube construction is increasingly becoming a significant bottleneck. In addition, with the popularity of cloud computing and big data, MapReduce framework proposed by Google is playing an increasingly prominent role in parallel processing. It is an intuitive idea that MapReduce framework can be used to enhance the efficiency of parallel data cube construction. In this paper, by improving the Frag-Shells algorithm based on the irrelevance of data dispersion, and taking advantages of the high parallelism of MapReduce framework, we propose an improved Frag-Shells algorithm based on MapReduce framework. The simulation results prove that the proposed algorithm greatly enhances the efficiency of cube construction.展开更多
The accurate tracing of multi-artificial recharge sources is crucial for assessing their impacts on groundwater quantity and quality in adjacent aquifers.However,conventional ions and emerging contaminants with non-co...The accurate tracing of multi-artificial recharge sources is crucial for assessing their impacts on groundwater quantity and quality in adjacent aquifers.However,conventional ions and emerging contaminants with non-conservative behavior exhibit limitations in tracing artificially recharged groundwater.Thus,there is an urgent need to identify effective tracers for delineating artificial recharge processes.This study focuses on the multi-artificial recharge area in Chaobai River,Beijing,being recharged by reclaimed water,South-to-North Water Diversion water,and Wenyu River-Chaobai Riverdiversion water.By investigating the attenuation patterns of typical tracers(e.g.,Gd/Gd*,Cl^(-)concentrations,and δ^(18)O values)along potential recharge pathways,we quantified the groundwater sources from different recharge zones and revealed their impacts on groundwater quality.Results show that,while hydrogeochemical types and major ion concentrations in groundwater across the three recharge zones were similar,Gd concentrations in groundwater influenced by reclaimed water were 40-50 times higher than those in the other two zones.Using Gd/Gd*as a tracer,the reclaimed water recharge extended laterally up to 1000 m from the river bank and to depths<60 m.The Wenyu River-Chaobai Riverdiversion water affected groundwater within 500 m laterally and<30 m vertically.A three-end-member mixing model based on Cl^(-)and δ^(18)O tracers revealed that the South-to-North Water Diversion recharge predominantly influenced groundwater within 2000 m laterally and<80 m in depth.High-arsenic groundwater was primarily distributed in the southern Wenyu River-Chaobai River diversion zone with limited recharge influence,where arsenic enrichment was linked to the reductive dissolution of arsenic-bearing Fe oxides and HCO_(3)^(-)-driven arsenic desorption.In contrast,groundwater arsenic concentrations were significantly lower in recharge-affected areas and showed a strong negative correlation with Gd/Gd*,indicating that artificial recharge effectively reduced groundwater arsenic levels.This study demonstrates the suitability of rare earth element anomalies(e.g.,Gd/Gd*)for tracing reclaimed water recharge and provides a scientific basis for assessing the impacts of multi-source artificial recharge on groundwater quality.展开更多
Rechargeable magnesium batteries(RMBs)have attracted much attention due to the high theoretical capacity(3833 mAh cm−3)of magnesium metal negative electrode and abundant resources.However,the preparation of ultra-thin...Rechargeable magnesium batteries(RMBs)have attracted much attention due to the high theoretical capacity(3833 mAh cm−3)of magnesium metal negative electrode and abundant resources.However,the preparation of ultra-thin magnesium foils faces the problems of rolling difficulty and high processing cost,while the use of thick magnesium foils leads to low utilization of magnesium and reduces the energy density.To tackle the above problems,we successfully prepared ultra-thin magnesium foils based on electrolytic process and investigated the effect of different substrates.The magnesium foils prepared using Mo substrate have more uniform surface morphology and lower surface roughness,which is attributed to the lower magnesium nucleation overpotential of Mo substrate.Meanwhile,density functional theory calculations show that the adsorption energy of Mo on Mg is more negative,which is conducive to achieving uniform nucleation and deposition of Mg.The Mg deposition on Mo substrate undergoes the characteristic stages of transient nucleation,nucleus accretion,multidirectional heterotopic growth,and columnar crystal stacking,and ultimately the formation of a dense deposited layer.In addition,the prepared ultra-thin Mg foil with Mo substrate can stably cycle for 1000 h at 3 mA cm^(-2) with high utilization of 50% in the symmetric cell.This study develops a facile method for the preparation of ultra-thin Mg foils,which opens up a new path for developing high-performance ultra-thin negative electrodes for RMBs.展开更多
Triangular voltage waveform was employed to distinguish the contributions of dielectric permittivity,electric conductivity and domain switching in current-electricfield curves.At the same time,it is shown how those co...Triangular voltage waveform was employed to distinguish the contributions of dielectric permittivity,electric conductivity and domain switching in current-electricfield curves.At the same time,it is shown how those contributions can affect the shape of the electric displacement-electricfield loops(D-E loops).The effects of frequency,temperature and microstructure(point defects,grain size and texture)on the ferroelectric properties of several ferroelectric compositions is reported,including.BaTiO_(3);lead zirconate titanate(PZT);lead-free Na_(0.5)K_(0.5)NbO_(3);perovskite-like layer structured A_(2)B_(2)O_(7)with super high Curie point(T_(c));Aurivillius phase ferroelectric Bi_(3.15)Nd_(0.85)Ti_(3)O_(12);and multiferroic Bi_(0.89)La_(0.05)Tb_(0.06)FeO_(3).This systematic study provides an instructive outline in the measurement of ferroelectric properties and the analysis and interpretation of experimental data.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.U2430204,U23A20567,and U2230119)the Outstanding Youth Science and Technology Talents Program of Sichuan Province,China(Grant No.22JCQN0005).
文摘Intersubband transition in ZnO/MgZnO quantum well has been exploited for infrared and terahertz optoelectronic applications due to its large band offset and fascinating material properties.Here,we theoretically demonstrate piezophototronic effect as another way to control the intersubband absorption wavelength through quantum-confined Stark effect.The intersubband optical absorption properties under different stresses are obtained by solving the eight-band k·p Hamiltonian and coupled Schr¨odinger-Poisson equations self-consistently.By combining stress control and quantum well structure,the absorption wavelength can show infrared blueshift or redshift phenomena in a wide range.This work can provide an effective avenue to control and utilize quantum-confined Stark effect in intersubband infrared absorption and promote the relative potential optoelectronic devices.
文摘The development of deep learning has revolutionized image recognition technology.How to design faster and more accurate image classification algorithms has become our research interests.In this paper,we propose a new algorithm called stochastic depth networks with deep energy model(SADIE),and the model improves stochastic depth neural network with deep energy model to provide attributes of images and analysis their characteristics.First,the Bernoulli distribution probability is used to select the current layer of the neural network to prevent gradient dispersion during training.Then in the backpropagation process,the energy function is designed to optimize the target loss function of the neural network.We also explored the possibility of using Adam and SGD combination optimization in deep neural networks.Finally,we use training data to train our network based on deep energy model and testing data to verify the performance of the model.The results we finally obtained in this research include the Classified labels of images.The impacts of our obtained results show that our model has high accuracy and performance.
基金the financial support of the National Natural Science Foundation of China[32201960,32073020]Science and Technology Innovation Program of Hunan Province[2022RC1150]+2 种基金Changsha Municipal Natural Science Foundation[kq2202332]Hunan innovative province construction project[2019NK2041]Agricultural Science and Technology Innovation Project of Hunan Province[2021CX05].
文摘The microbial potential of Penicillium has received critical attention.The present research aimed to elucidate the efficacy of crude enzyme secreted from Penicillium oxalicum WX-209 in degrading citrus segments and evaluate the safety of the process.Results showed that citrus segment membranes gradually dissolved after treatment with the crude enzyme solution,indicating good degradation capability.No significant differences in body weight,food ingestion rate,hematology,blood biochemistry,and weight changes of different organs were found between the enzyme intake and control groups.Serial experiments showed that the crude enzyme had high biological safety.Moreover,the whole genome of P.oxalicum WX-209 was sequenced by PacBio and Illumina platforms.Twenty-five scaffolds were assembled to generate 36 Mbp size of genome sequence comprising 11369 predicted genes modeled with a GC content of 48.33%.A total of 592 genes were annotated to encode enzymes related to carbohydrates,and some degradation enzyme genes were identified in strain P.oxalicum WX-209.
基金supported by the National Key Re-search and Development Program of China(No.2022YFB3807402)the National Natural Science Foundation of China(Nos.U23A20567,12204327,52061130216,52032007,and 52002252)+1 种基金the Fundamental Research Funds for the Central Universities(No.YJ2021154)the Natural Science Foundation of Sichuan Province(No.2023NSFSC0967).
文摘The rapid growth of new electromechanical applications has increased the demand for ferroelectric ce-ramics with excellent piezoelectric properties and a wide temperature operating range.However,achiev-ing robust piezoelectricity and temperature stability simultaneously in lead zirconate titanate(Pb(Zr,Ti)O_(3))based piezoelectric ceramics poses a significant challenge.This study proposes a new material system of 0.98Pb_(0.84)Ba_(0.16)(Zr_(2/3)Ti_(1/3))O_(3)-0.02Pb(Sb_(1/2)Nb_(1/2))O_(3)+x wt.%Sm_(2)O_(3)(PBZT-PSN-x Sm)to address this challenge.Remarkably,the ceramics x=0.4 demonstrate excellent piezoelectric properties(includ-ing piezoelectric coefficient d33 of 610 pC/N and Curie temperature TC of 290℃)and favorable tem-perature stability(i.e.,d33 varies less than 10%within 25-200℃).The high piezoelectric properties arise from the optimized phase fraction between rhombohedral(R)and tetragonal(T)phases and the increased grain size,which enhance the lattice distortion and the domain switching under electric fields,respectively.The superior temperature stability can be attributed to stable crystal structure and domain structure.These findings indicate that PBZT-PSN-x Sm ceramics hold great promise for practical utilization in high-temperature transducers and sensors.
文摘Maintenance is an important aspect in the lifecycle of communication network devices.Prevalent problems in the maintenance of communication networks include inconvenient data carrying and sub-optimal scheduling of work orders,which significantly restrict the efficiency of maintenance work.Moreover,most maintenance systems are still based on cloud architectures that slow down data transfer.With a focus on the completion time,quality,and load balancing of maintenance work,we propose in this paper a learning-based virus evolutionary genetic algorithm with multiple quality-ofservice(QoS)constraints to implement intelligent scheduling in an edge network.The algorithm maintains the diversity of the population and improves the speed of convergence using a fitness function and a learning-based population generation mechanism.The test results demonstrate that the algorithm delivers good performance in terms of load balancing and QoS guarantee.We also propose a knowledge push algorithm based on a context model for intelligently pushing relevant knowledge according to the given conditions.The simulation results demonstrate that our scheme can improve the efficiency of on-site maintenance.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2230119 and U23A20567)2022 Central Guidance on Local Science and Technology Development Projects(Grant No.2022ZYDF073)Outstanding Youth Fund of Sichuan Province(Grant No.22JCQN0005).
文摘It has been known that metal FeSiCr powders with large average particle sizes have been typically employed to prepare magnetic powder cores(SMCs),with few studies reported on the influence of magnetic properties for original powders with various average particle sizes less than 10m.In this work,SiO_(2)-coated FeSiCr SMCs with different small particle sizes were synthesized using the sol-gel process.The contribution of SiO_(2)coating amount and voids to the soft magnetic properties was elaborated.The mechanism was revealed such that smaller particle sizes with less voids could be beneficial for reducing core loss in the SMCs.By optimizing the core structure,permeability and magnetic loss of 26 and 262 kW/cm^(3)at 100 kHz and 50 mT were achieved at a particle size of 4.8m and ethyl orthosilicate addition of 0.1 mL/g.The best DC stacking performance,reaching 87%,was observed at an ethyl orthosilicate addition rate of 0.25 mL/g under 100 Oe.Compared to other soft magnetic composites(SMCs),the FeSiCr/SiO_(2)SMCs exhibit significantly reduced magnetic loss.It further reduces the magnetic loss of the powder core,providing a new strategy for applications of SMCs at high frequencies.
基金the support of the Nature Science Foundation of China(Grant No.11372208,11502157)Shanxi Province Programs for Science and Technology Innovation in Colleges and Universities(Grant No.2016BY062).
文摘Arterial characterization of healthy descending thoracic aorta(DTA)is indispensable in determining stress distributions across wall thickness and different regions that may be responsible for aorta inhomogeneous dilation,rupture,and dissection when aneurysm occurs.Few studies have shown the inhomogeneity of DTA along the aorta tree considering changes in circumferential direction.The present study aims to clarify the circumferential regional characterization of DTA.Porcine DTA tissues were tested according to region and orientation using uniaxial tension.For axial test,results show that the difference in circumferential direction was mainly in collagen fiber modulus,where the anterior collagen fiber modulus was significantly lower than the posterior quadrant.For circumferential test,the difference in circumferential direction was mainly in the recruitment parameter,where the circumferential stiffness is significantly higher in the posterior region at physiological maximum stress.The proximal posterior quadrant and left quadrant showed significantly lower axial collagen fiber stiffness than the right and anterior quadrants,which may be a factor in aneurysm development.Furthermore,the constitutive parameters for similar detailed regions can be used by biomedical engineers to investigate improved therapies and thoroughly understand the initial stage of aneurysm development.The regional collagen fiber modulus can help improve our understanding of the mechanisms of arterial dilation and aortic dissection.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11904299 and U1930124)the Foundation of China Academy of Engineering Physics(Grant No.2018AB02)。
文摘Manipulating metal-insulator transitions in strongly correlated materials is of great importance in condensed matter physics,with implications for both fundamental science and technology.Vanadium dioxide(VO_(2)),as an ideal model system,is metallic at high temperatures and shown a typical metal-insulator structural phase transition at 341 K from rutile structure to monoclinic structure.This behavior has been absorbed tons of attention for years.However,how to control this phase transition is still challenging and little studied.Here we demonstrated that to control the Ag nanonet arrays(NAs)in monoclinic VO_(2)(M)could be effective to adjust this metal-insulator transition.With the increase of Ag NAs volume fraction by reducing the template spheres size,the transition temperature(Tc)decreased from 68°C to 51°C.The mechanism of Tc decrease was revealed as:the carrier density increases through the increase of Ag NAs volume fraction,and more free electrons injected into the VO_(2)films induced greater absorption energy at the internal nanometal-semiconductor junction.These results supply a new strategy to control the metal-insulator transitions in VO_(2),which must be instructive for the other strongly correlated materials and important for applications.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11904299,U1930124,and 11804312)China Academy of Engineering Physics(CAEP)Foundation(Grant No.2018AB02)。
文摘Size and morphology are critical factors in determining the electrochemical performance of the supercapacitor materials,due to the manifestation of the nanosize effect.Herein,different nanostructures of the CrN material are prepared by the combination of a thermal-nitridation process and a template technique.High-temperature nitridation could not only transform the hexagonal Cr_(2)O_(3)into cubic CrN,but also keep the template morphology barely unchanged.The obtained CrN nanostructures,including(i)hierarchical microspheres assembled by nanoparticles,(ii)microlayers,and(iii)nanoparticles,are studied for the electrochemical supercapacitor.The CrN microspheres show the best specific capacitance(213.2 F/g),cyclic stability(capacitance retention rate of 96%after 5000 cycles in 1-mol/L KOH solution),high energy density(28.9 Wh/kg),and power density(443.4 W/kg),comparing with the other two nanostructures.Based on the impedance spectroscopy and nitrogen adsorption analysis,it is revealed that the enhancement arised mainly from a high-conductance and specific surface area of CrN microspheres.This work presents a general strategy of fabricating controllable CrN nanostructures to achieve the enhanced supercapacitor performance.
基金supported by National Key R&D Program of China(2019YFB2103202).
文摘Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering the unique characteristics of edge networks,such as limited resources,complex network faults,and the need for high real-time performance,enhancing and optimizing existing network fault diagnosis methods is necessary.Therefore,this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network(LSNN).Firstly,we use the Izhikevich neurons model to replace the Leaky Integrate and Fire(LIF)neurons model in the LSNN model.Izhikevich neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data inputs.Inspired by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of FSIs.Secondly,inspired by the connection mode based on spiking dynamics in the basal ganglia(BG)area of the brain,we propose the pruning approach based on the FSIs of the BG in LSNN to improve computational efficiency and reduce the demand for computing resources and energy consumption.Furthermore,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN model.Experiments on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis dimensions.Simultaneously,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model efficiency.To conclude,experiment results on a pair of datasets indicate that the LSNN model surpasses traditional models and achieves cutting-edge outcomes in network fault diagnosis tasks.
基金supported by the National Key R&D Program of China(2019YFB2103202).
文摘Nowadays,ensuring thequality of networkserviceshas become increasingly vital.Experts are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device configurations.This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms.Initially,an improved active learning approach is employed to select the most valuable unlabeled samples,which are subsequently submitted for expert labeling.This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set.Then the labeled samples are utilized to train the model for network configuration entity extraction.Furthermore,the multi-head self-attention of the transformer model is enhanced by introducing the Adaptive Weighting method based on the Laplace mixture distribution.This enhancement enables the transformer model to dynamically adapt its focus to words in various positions,displaying exceptional adaptability to abnormal data and further elevating the accuracy of the proposed model.Through comparisons with Random Sampling(RANDOM),Maximum Normalized Log-Probability(MNLP),Least Confidence(LC),Token Entrop(TE),and Entropy Query by Bagging(EQB),the proposed method,Entropy Query by Bagging and Maximum Influence Active Learning(EQBMIAL),achieves comparable performance with only 40% of the samples on both datasets,while other algorithms require 50% of the samples.Furthermore,the entity extraction algorithm with the Adaptive Weighted Multi-head Attention mechanism(AW-MHA)is compared with BILSTM-CRF,Mutil_Attention-Bilstm-Crf,Deep_Neural_Model_NER and BERT_Transformer,achieving precision rates of 75.98% and 98.32% on the two datasets,respectively.Statistical tests demonstrate the statistical significance and effectiveness of the proposed algorithms in this paper.
基金supported by the National Key R&D Program of China(2019YFB2103202).
文摘With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant trend.The existing fault diagnosismethods have the characteristics of heavy computing and storage load,and most of them have computational redundancy,which is not suitable for deployment on edge devices with limited resources and capabilities.This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation.First,we offer a clustering-based self-knowledge distillation approach(Cluster KD),which takes the mean value of the sample diagnosis results,clusters them,and takes the clustering results as the terms of the loss function.It utilizes the correlations between faults of the same type to improve the accuracy of the teacher model,especially for fault categories with high similarity.Then,the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweightmodel for edge-side deployment.We propose a two-stage edge-side fault diagnosismethod(TSM)that separates fault detection and fault diagnosis into different stages:in the first stage,a fault detection model based on a denoising auto-encoder(DAE)is adopted to achieve fast fault responses;in the second stage,a diverse convolutionmodel with variance weighting(DCMVW)is used to diagnose faults in detail,extracting features frommicro andmacro perspectives.Through comparison experiments conducted on two fault datasets,it is proven that the proposed method has high accuracy,low delays,and small computation,which is suitable for intelligent edge-side fault diagnosis.In addition,experiments show that our approach has a smooth training process and good balance.
基金the General Program of National Natural Science Foundation of China(62072049).
文摘The rapid growth of modern mobile devices leads to a large number of distributed data,which is extremely valuable for learning models.Unfortunately,model training by collecting all these original data to a centralized cloud server is not applicable due to data privacy and communication costs concerns,hindering artificial intelligence from empowering mobile devices.Moreover,these data are not identically and independently distributed(Non-IID)caused by their different context,which will deteriorate the performance of the model.To address these issues,we propose a novel Distributed Learning algorithm based on hierarchical clustering and Adaptive Dataset Condensation,named ADC-DL,which learns a shared model by collecting the synthetic samples generated on each device.To tackle the heterogeneity of data distribution,we propose an entropy topsis comprehensive tiering model for hierarchical clustering,which distinguishes clients in terms of their data characteristics.Subsequently,synthetic dummy samples are generated based on the hierarchical structure utilizing adaptive dataset condensation.The procedure of dataset condensation can be adjusted adaptively according to the tier of the client.Extensive experiments demonstrate that the performance of our ADC-DL is more outstanding in prediction accuracy and communication costs compared with existing algorithms.
基金supported the earmarked fund for China Agriculture Research System of MOF and MARA(CARS-45)Natural Sciences Foundation of China(32073019)。
文摘Myxozoans are common microscopic endoparasites in fish,and some are highly pathogenic to their wild and farmed fish hosts.In the present study,myxosporeans infection in the kidney of silver carp Hypophthalmichthys molitrix(Valenciennes,1844)from Lake Taihu,was investigated,and two dominate species,Myxobolus lieni(Nie&Li,1973)and M.varius(Achmerov,1960),with infection prevalence 60.2%and 35.2%,respectively,were well characterized based on morphological,histopathological and DNA sequence data.M.lieni formed small roundish plasmodia in the epithelial cells of renal tubules.The mature myxospores appeared suborbicular,slightly flat in frontal view and fusiform shaped in sutural view.Dispersed myxospores of M.varius were found in the renal interstitium without forming plasmodia structures and enclosed within melano-macrophage centers.The spore appeared elliptical in frontal view,with wider anterior than posterior and shuttle shaped in sutural view.Interestingly,the occurrence of myxozoans in the kidney detected with SSU rDNA PCR and clone sequencing,revealed co-infection of five Myxobolus species.BLASTn search indicated SSU r DNA gene sequences obtained here were not identical to any sequence available in GenBank.Phylogenetic analyses showed that the five Myxobolus species detected here were clustered together,forming a separate clade of cyprinid-infecting myxozoans.
基金supported by National Key R&D Program of China(2020YFB1807800).
文摘Crowdsensing,as a data collection method that uses the mobile sensing ability of many users to help the public collect and extract useful information,has received extensive attention in data collection.Since crowdsensing relies on user equipment to consume resources to obtain information,and the quality and distribution of user equipment are uneven,crowdsensing has problems such as low participation enthusiasm of participants and low quality of collected data,which affects the widespread use of crowdsensing.This paper proposes to apply the blockchain to crowdsensing and solve the above challenges by utilizing the characteristics of the blockchain,such as immutability and openness.An architecture for constructing a crowdsensing incentive mechanism under distributed incentives is proposed.A multi-attribute auction algorithm and a k-nearest neighbor-based sensing data quality determination algorithm are proposed to support the architecture.Participating users upload data,determine data quality according to the algorithm,update user reputation,and realize the selection of perceived data.The process of screening data and updating reputation value is realized by smart contracts,which ensures that the information cannot be tampered with,thereby encouraging more users to participate.Results of the simulation show that using two algorithms can well reflect data quality and screen out malicious data.With the help of blockchain performance,the architecture and algorithm can achieve decentralized storage and tamper-proof information,which helps to motivate more users to participate in perception tasks and improve data quality.
基金This paper is supported by the National Natural Science Foundation of China (61272515), and National Science & Technology Pillar Program (2015BAH03F02).
文摘Real-time Fraud Detection has always been a challenging task, especially in financial, insurance, and telecom industries. There are mainly three methods, which are rule set, outlier detection and classification to solve the problem. But those methods have some drawbacks respectively. To overcome these limitations, we propose a new algorithm UAF (Usage Amount Forecast).Firstly, Manhattan distance is used to measure the similarity between fraudulent instances and normal ones. Secondly, UAF gives real-time score which detects the fraud early and reduces as much economic loss as possible. Experiments on various real-world datasets demonstrate the high potential of UAF for processing real-time data and predicting fraudulent users.
文摘On-Line Analytical Processing (OLAP) is based on pre-computation of data cubes, which greatly reduces the response time and improves the performance of OLAP. Frag-Shells algorithm is a common method of precomputation.However, it relies too much on the data dispersion that it performs poorly, when confronts large amount of highly disperse data. As the amount of data grows fast nowadays, the efficiency of data cube construction is increasingly becoming a significant bottleneck. In addition, with the popularity of cloud computing and big data, MapReduce framework proposed by Google is playing an increasingly prominent role in parallel processing. It is an intuitive idea that MapReduce framework can be used to enhance the efficiency of parallel data cube construction. In this paper, by improving the Frag-Shells algorithm based on the irrelevance of data dispersion, and taking advantages of the high parallelism of MapReduce framework, we propose an improved Frag-Shells algorithm based on MapReduce framework. The simulation results prove that the proposed algorithm greatly enhances the efficiency of cube construction.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFA0715902)the National Natural Science Foundation of China(Grant Nos.42130509,U2444218)。
文摘The accurate tracing of multi-artificial recharge sources is crucial for assessing their impacts on groundwater quantity and quality in adjacent aquifers.However,conventional ions and emerging contaminants with non-conservative behavior exhibit limitations in tracing artificially recharged groundwater.Thus,there is an urgent need to identify effective tracers for delineating artificial recharge processes.This study focuses on the multi-artificial recharge area in Chaobai River,Beijing,being recharged by reclaimed water,South-to-North Water Diversion water,and Wenyu River-Chaobai Riverdiversion water.By investigating the attenuation patterns of typical tracers(e.g.,Gd/Gd*,Cl^(-)concentrations,and δ^(18)O values)along potential recharge pathways,we quantified the groundwater sources from different recharge zones and revealed their impacts on groundwater quality.Results show that,while hydrogeochemical types and major ion concentrations in groundwater across the three recharge zones were similar,Gd concentrations in groundwater influenced by reclaimed water were 40-50 times higher than those in the other two zones.Using Gd/Gd*as a tracer,the reclaimed water recharge extended laterally up to 1000 m from the river bank and to depths<60 m.The Wenyu River-Chaobai Riverdiversion water affected groundwater within 500 m laterally and<30 m vertically.A three-end-member mixing model based on Cl^(-)and δ^(18)O tracers revealed that the South-to-North Water Diversion recharge predominantly influenced groundwater within 2000 m laterally and<80 m in depth.High-arsenic groundwater was primarily distributed in the southern Wenyu River-Chaobai River diversion zone with limited recharge influence,where arsenic enrichment was linked to the reductive dissolution of arsenic-bearing Fe oxides and HCO_(3)^(-)-driven arsenic desorption.In contrast,groundwater arsenic concentrations were significantly lower in recharge-affected areas and showed a strong negative correlation with Gd/Gd*,indicating that artificial recharge effectively reduced groundwater arsenic levels.This study demonstrates the suitability of rare earth element anomalies(e.g.,Gd/Gd*)for tracing reclaimed water recharge and provides a scientific basis for assessing the impacts of multi-source artificial recharge on groundwater quality.
基金supported by the National Natural Science Foundation of China(No.U2037601)the National Key Research and Development Program(No.2023YFB3809500)the Chongqing Technology Innovation and Application Development Project(No.CSTB2022TIAD-KPX0028).
文摘Rechargeable magnesium batteries(RMBs)have attracted much attention due to the high theoretical capacity(3833 mAh cm−3)of magnesium metal negative electrode and abundant resources.However,the preparation of ultra-thin magnesium foils faces the problems of rolling difficulty and high processing cost,while the use of thick magnesium foils leads to low utilization of magnesium and reduces the energy density.To tackle the above problems,we successfully prepared ultra-thin magnesium foils based on electrolytic process and investigated the effect of different substrates.The magnesium foils prepared using Mo substrate have more uniform surface morphology and lower surface roughness,which is attributed to the lower magnesium nucleation overpotential of Mo substrate.Meanwhile,density functional theory calculations show that the adsorption energy of Mo on Mg is more negative,which is conducive to achieving uniform nucleation and deposition of Mg.The Mg deposition on Mo substrate undergoes the characteristic stages of transient nucleation,nucleus accretion,multidirectional heterotopic growth,and columnar crystal stacking,and ultimately the formation of a dense deposited layer.In addition,the prepared ultra-thin Mg foil with Mo substrate can stably cycle for 1000 h at 3 mA cm^(-2) with high utilization of 50% in the symmetric cell.This study develops a facile method for the preparation of ultra-thin Mg foils,which opens up a new path for developing high-performance ultra-thin negative electrodes for RMBs.
基金This work was supported by the UK Royal Society International Joint Project(2009/R2)Xi'an Jiaotong University international incoming short visits(2008 and 2009).
文摘Triangular voltage waveform was employed to distinguish the contributions of dielectric permittivity,electric conductivity and domain switching in current-electricfield curves.At the same time,it is shown how those contributions can affect the shape of the electric displacement-electricfield loops(D-E loops).The effects of frequency,temperature and microstructure(point defects,grain size and texture)on the ferroelectric properties of several ferroelectric compositions is reported,including.BaTiO_(3);lead zirconate titanate(PZT);lead-free Na_(0.5)K_(0.5)NbO_(3);perovskite-like layer structured A_(2)B_(2)O_(7)with super high Curie point(T_(c));Aurivillius phase ferroelectric Bi_(3.15)Nd_(0.85)Ti_(3)O_(12);and multiferroic Bi_(0.89)La_(0.05)Tb_(0.06)FeO_(3).This systematic study provides an instructive outline in the measurement of ferroelectric properties and the analysis and interpretation of experimental data.
