High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys

Magnesium(Mg)alloys have shown great prospects as both structural and biomedical materials,while poor corrosion resistance limits their further application.In this work,to avoid the time-consuming and laborious experiment trial,a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics,from both the thermodynamic and kinetic perspectives.The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified.Then,the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated,and the corrosion exchange current density is further calculated by a hydrogen evolution reaction(HER)kinetic model.Several intermetallics,e.g.Y_(3)Mg,Y_(2)Mg and La_(5)Mg,are identified to be promising intermetallics which might effectively hinder the cathodic HER.Furthermore,machine learning(ML)models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function(W_(f))and weighted first ionization energy(WFIE).The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error(RMSE)of 0.11 eV.This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion,but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy,which can be extended to ternary Mg alloys or other alloy systems.

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

Jamming Assisted Covert Wireless Communication in Multi-Channel Systems

This paper investigates the jammerassisted multi-channel covert wireless communication(CWC)by exploiting the randomness of sub-channel selection to confuse the warden.In particular,we propose two sub-channel selection transmission schemes,named random sub-channel selection(RSS)scheme and maximum sub-channel selection(MSS)scheme,to enhance communication covertness.For each proposed scheme,we first derive closed-form expressions of the transmission outage probability(TOP),the average effective rate,and the minimum average detection error probability(DEP).Then,the average effective covert rate(ECR)is maximized by jointly optimizing the transmit power at the transmitter and the number of sub-channels.Numerical results show that there is an optimal value of the number of sub-channels that maximizes the average ECR.We also find that to achieve the maximum average ECR,a larger number of subchannels are needed facing a stricter covertness constraint.

From the perspective of experimental practice: High-throughput computational screening in photocatalysis

Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is now generating widespread interest in boosting the conversion effi-ciency of solar energy.In the past decade,computational technologies and theoretical simulations have led to a major leap in the development of high-throughput computational screening strategies for novel high-efficiency photocatalysts.In this viewpoint,we started with introducing the challenges of photocatalysis from the view of experimental practice,especially the inefficiency of the traditional“trial and error”method.Sub-sequently,a cross-sectional comparison between experimental and high-throughput computational screening for photocatalysis is presented and discussed in detail.On the basis of the current experimental progress in photocatalysis,we also exemplified the various challenges associated with high-throughput computational screening strategies.Finally,we offered a preferred high-throughput computational screening procedure for pho-tocatalysts from an experimental practice perspective(model construction and screening,standardized experiments,assessment and revision),with the aim of a better correlation of high-throughput simulations and experimental practices,motivating to search for better descriptors.

Development of a High-throughput Sequencing Platform for Detection of Viral Encephalitis Pathogens Based on Amplicon Sequencing

Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing.Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing.Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing.Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.

High-throughput microfluidic production of carbon capture microcapsules:fundamentals,applications,and perspectives

In the last three decades,carbon dioxide(CO_(2)) emissions have shown a significant increase from various sources.To address this pressing issue,the importance of reducing CO_(2) emissions has grown,leading to increased attention toward carbon capture,utilization,and storage strategies.Among these strategies,monodisperse microcapsules,produced by using droplet microfluidics,have emerged as promising tools for carbon capture,offering a potential solution to mitigate CO_(2) emissions.However,the limited yield of microcapsules due to the inherent low flow rate in droplet microfluidics remains a challenge.In this comprehensive review,the high-throughput production of carbon capture microcapsules using droplet microfluidics is focused on.Specifically,the detailed insights into microfluidic chip fabrication technologies,the microfluidic generation of emulsion droplets,along with the associated hydrodynamic considerations,and the generation of carbon capture microcapsules through droplet microfluidics are provided.This review highlights the substantial potential of droplet microfluidics as a promising technique for large-scale carbon capture microcapsule production,which could play a significant role in achieving carbon neutralization and emission reduction goals.

A novel multi-channel porous structure facilitating mass transport towards highly efficient alkaline water electrolysis

An advantageous porous architecture of electrodes is pivotal in significantly enhancing alkaline water electrolysis(AWE)efficiency by optimizing the mass transport mechanisms.This effect becomes even more pronounced when aiming to achieve elevated current densities.Herein,we employed a rapid and scalable laser texturing process to craft novel multi-channel porous electrodes.Particularly,the obtained electrodes exhibit the lowest Tafel slope of 79 mV dec^(-1)(HER)and 49 mV dec^(-1)(OER).As anticipated,the alkaline electrolyzer(AEL)cell incorporating multi-channel porous electrodes(NP-LT30)exhibited a remarkable improvement in cell efficiency,with voltage drops(from 2.28 to 1.97 V)exceeding 300 mV under 1 A cm^(-1),compared to conventional perforated Ni plate electrodes.This enhancement mainly stemmed from the employed multi-channel porous structure,facilitating mass transport and bubble dynamics through an innovative convection mode,surpassing the traditional convection mode.Furthermore,the NP-LT30-based AEL cell demonstrated exceptional durability for 300 h under 1.0 A cm^(-2).This study underscores the capability of the novel multi-channel porous electrodes to expedite mass transport in practical AWE applications.

High-throughput calculation-based rational design of Fe-doped MoS_(2) nanosheets for electrocatalytic p H-universal overall water splitting

Electrocatalytic water splitting is crucial for H2generation via hydrogen evolution reaction(HER)but subject to the sluggish dynamics of oxygen evolution reaction(OER).In this work,single Fe atomdoped MoS_(2)nanosheets(SFe-DMNs)were prepared based on the high-throughput density functional theory(DFT)calculation screening.Due to the synergistic effect between Fe atom and MoS_(2)and optimized intermediate binding energy,the SFe-DMNs could deliver outstanding activity for both HER and OER.When assembled into a two-electrode electrolytic cell,the SFe-DMNs could achieve the current density of 50 mA cm^(-2)at a low cell voltage of 1.55 V under neutral condition.These results not only confirmed the effectiveness of high-throughput screening,but also revealed the excellent activity and thus the potential applications in fuel cells of SFe-DMNs.

High-throughput computational screening and in vitro evaluation identifies 5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione(C3),as a novel EGFR—HER2 dual inhibitor in gastric tumors

Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation,adhesion,angiogenesis,and metastasis.Conventional therapies are ineffective due to the intra-tumoral heterogeneity and concomitant genetic mutations.Hence,dual inhibition strategies are recommended to increase potency and reduce cytotoxicity.In this study,we have conducted computational high-throughput screening of the ChemBridge library followed by in vitro assays and identified novel selective inhibitors that have a dual impediment of EGFR/HER2 kinase activities.Diversity-based High-throughput Virtual Screening(D-HTVS)was used to screen the whole ChemBridge small molecular library against EGFR and HER2.The atomistic molecular dynamic simulation was conducted to understand the dynamics and stability of the protein-ligand complexes.EGFR/HER2 kinase enzymes,KATOIII,and Snu-5 cells were used for in vitro validations.The atomistic Molecular Dynamics simulations followed by solvent-based Gibbs binding free energy calculation of top molecules,identified compound C3(5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione)to have a good affinity for both EGFR and HER2.The predicted compound,C3,was promising with better binding energy,good binding pose,and optimum interactions with the EGFR and HER2 residues.C3 inhibited EGFR and HER2 kinases with IC50 values of 37.24 and 45.83 nM,respectively.The GI50 values of C3 to inhibit KATOIII and Snu-5 cells were 84.76 and 48.26 nM,respectively.Based on these findings,we conclude that the identified compound C3 showed a conceivable dual inhibitory activity on EGFR/HER2 kinase,and therefore can be considered as a plausible lead-like molecule for treating gastric cancers with minimal side effects,though testing in higher models with pharmacokinetic approach is required.

Variation of microbiological and small molecule metabolite profiles of Nuodeng ham during ripening by high-throughput sequencing and GC-TOF-MS

The internal microbial diversity and small molecular metabolites of Nuodeng ham in different processing years(the first,second and third year sample)were analyzed by high-throughput sequencing technology and gas chromatography-time of flight mass spectrography(GC-TOF-MS)to study the effects of microorganisms and small molecular metabolites on the quality of ham in different processing years.The results showed that the dominant bacteria phyla of Nuodeng ham in different processing years were Proteobacteria and Firmicutes,the dominant fungi phyla were Ascomycota and Basidiomycota,while Staphylococcus and Aspergillus were the dominant bacteria and fungi of Nuodeng ham,respectively.Totally,252 kinds of small molecular metabolites were identified from Nuodeng ham in different processing years,and 12 different metabolites were screened through multivariate statistical analysis.Further metabolic pathway analysis showed that 23 metabolic pathways were related to ham fermentation,of which 8 metabolic pathways had significant effects on ham fermentation(Impact>0.01,P<0.05).The content of L-proline,phenyllactic acid,L-lysine,carnosine,taurine,D-proline,betaine and creatine were significantly positively correlated with the relative abundance of Staphylococcus and Serratia,but negatively correlated with the relative abundance of Halomonas,Aspergillus and Yamadazyma.

High-throughput screening system of citrus bacterial cankerassociated transcription factors and its application to the regulation of citrus canker resistance

One of the main diseases that adversely impacts the global citrus industry is citrus bacterial canker(CBC),caused by the bacteria Xanthomonas citri subsp.citri(Xcc).Response to CBC is a complex process,with both proteinDNA as well as protein–protein interactions for the regulatory network.To detect such interactions in CBC resistant regulation,a citrus high-throughput screening system with 203 CBC-inducible transcription factors(TFs),were developed.Screening the upstream regulators of target by yeast-one hybrid(Y1H)methods was also performed.A regulatory module of CBC resistance was identified based on this system.One TF(CsDOF5.8)was explored due to its interactions with the 1-kb promoter fragment of CsPrx25,a resistant gene of CBC involved in reactive oxygen species(ROS)homeostasis regulation.Electrophoretic mobility shift assay(EMSA),dual-LUC assays,as well as transient overexpression of CsDOF5.8,further validated the interactions and transcriptional regulation.The CsDOF5.8–CsPrx25 promoter interaction revealed a complex pathway that governs the regulation of CBC resistance via H2O2homeostasis.The high-throughput Y1H/Y2H screening system could be an efficient tool for studying regulatory pathways or network of CBC resistance regulation.In addition,it could highlight the potential of these candidate genes as targets for efforts to breed CBC-resistant citrus varieties.

Design of Cold-Junction Compensation and Disconnection Detection Circuits of Various Thermocouples and Implementation of Multi-Channel Interfaces Using Them-A Secondary Publication

Cold-junction compensation(CJC)and disconnection detection circuit design of various thermocouples(TC)and multi-channel TC interface circuits were designed.The CJC and disconnection detection circuit consists of a CJC semiconductor device,an instrumentation amplifier(IA),two resistors,and a diode for disconnection detection.Based on the basic circuit,a multi-channel interface circuit was also implemented.The CJC was implemented using compensation semiconductor and IA,and disconnection detection was detected by using two resistors and a diode so that IA input voltage became-0.42 V.As a result of the experiment using R-type TC,the error of the designed circuit was reduced from 0.14 mV to 3μV after CJC in the temperature range of 0°C to 1400°C.In addition,it was confirmed that the output voltage of IA was saturated from 88 mV to-14.2 V when TC was disconnected from normal.The output voltage of the designed circuit was 0 V to 10 V in the temperature range of 0°C to 1400°C.The results of the 4-channel interface experiment using R-type TC were almost identical to the CJC and disconnection detection results for each channel.The implemented multi-channel interface has a feature that can be applied equally to E,J,K,T,R,and S-type TCs by changing the terminals of CJC semiconductor devices and adjusting the IA gain.

Multi-channel terahertz focused beam generator based on shared-aperture metasurface

Most of existing metasurfaces usually have limited channel behavior,which seriouslyhinders their development and application.In this paper,we propose a multi-channel terahertz focused beam generator based on shared-aperture metasurface,and the generator consists of a top square metal strip,a middle layer of silica and a metal bottom plate.By changing the position and size of the shared-aperture array,the designed metasurface can generate any number of multi-channel focusing beams at different predicted positions.In addition,the energy intensity of focusing beams can be controlled.The full-wave simulation results show that the metasurface achieves four-channel vortex focused beam generation with different topological charges,and five-,six-,eight-channel focused beam generation with different energy intensities at a frequency of 1 THz,which are in good agreement with the theoretically calculated predictions.This work can provide a new idea for designing the terahertz multichannel devices.

Transfer learning aided high-throughput computational design of oxygen evolution reaction catalysts in acid conditions

Sluggish oxygen evolution reaction(OER)in acid conditions is one of the bottlenecks that prevent the wide adoption of proton exchange membrane water electrolyzer for green hydrogen production.Despite recent advancements in developing high-performance catalysts for acid OER,the current electrocatalysts still rely on iridium-and ruthenium-based materials,urging continuous efforts to discover better performance catalysts as well as reduce the usage of noble metals.Pyrochlore structured oxide is a family of potential high-performance acid OER catalysts with a flexible compositional space to tune the electrochemical capabilities.However,exploring the large composition space of pyrochlore compounds demands an imperative approach to enable efficient screening.Here we present a highthroughput screening pipeline that integrates density functional theory calculations and a transfer learning approach to predict the critical properties of pyrochlore compounds.The high-throughput screening recommends three sets of candidates for potential acid OER applications,totaling 61 candidates from 6912 pyrochlore compounds.In addition to 3d-transition metals,p-block metals are identified as promising dopants to improve the catalytic activity of pyrochlore oxides.This work demonstrates not only an efficient approach for finding suitable pyrochlores towards acid OER but also suggests the great compositional flexibility of pyrochlore compounds to be considered as a new materials platform for a variety of applications.

High-throughput sorting of two-color fluorescent-labeled zebrafish embryos

The zebrafish embryos were widely employed in genetics,development and drug discovery studies as miniatured animal models.Sorting of two-color fluorescent embryos is often required in large-scale experiments but it is challenging to manually sort with high efficiency.Here,we reported a high-throughput sorting system for two-color fluorescent zebraflsh embryos.The embryos can be automatically loaded from a sample pool and sorted based on the average fluorescent intensity.The two-color fluorescent signals were split into two lines and detected by an area array camera.The system achieves the sorting of 100 embryos in less than 10 min with an accuracy of greater than 95%.

Fast pre-stack multi-channel inversion constrained by seismic reflection features

Classical multi-channel technology can significantly reduce the pre-stack seismic inversion uncertainty, especially for complex geology such as high dipping structures. However, due to the consideration of complex structure or reflection features, the existing multi-channel inversion methods have to adopt the highly time-consuming strategy of arranging seismic data trace-by-trace, limiting its wide application in pre-stack inversion. A fast pre-stack multi-channel inversion constrained by seismic reflection features has been proposed to address this issue. The key to our method is to re-characterize the reflection features to directly constrain the pre-stack inversion through a Hadamard product operator without rearranging the seismic data. The seismic reflection features can reflect the distribution of the stratum reflection interface, and we obtained them from the post-stack profile by searching the shortest local Euclidean distance between adjacent seismic traces. Instead of directly constructing a large-size reflection features constraint operator advocated by the conventional methods, through decomposing the reflection features along the vertical and horizontal direction at a particular sampling point, we have constructed a computationally well-behaved constraint operator represented by the vertical and horizontal partial derivatives. Based on the Alternating Direction Method of Multipliers (ADMM) optimization, we have derived a fast algorithm for solving the objective function, including Hadamard product operators. Compared with the conventional reflection features constrained inversion, the proposed method is more efficient and accurate, proved on the Overthrust model and a field data set.

Development and Characterization of Microsatellite Markers for Harpadon nehereus Based on High-Throughput Sequencing and Cross-Species Amplification in Three Myctophiformes Fishes

Harpadon nehereus is a widespread economical fish found in the coastal seas of China and has important ecological value in the marine ecosystem.H_(o)wever,its germplasm resources have been seriously degraded due to natural factors and anthropogenic activities.In this study,high-throughput sequencing was applied to search for microsatellite loci in H.nehereus transcriptome to provide references for its resource conservation and utilization.Polymorphic loci were developed by non-denaturing polyacrylamide gel electrophoresis,and their cross-species amplified ability was detected in three related species.A total of 5652 microsatellites were identified from 16974320 unigenes.Among the primer pairs designed for 100 SSRs for PCR amplification,80%were successfully amplified,and 26 loci were polymorphic with a high number of alleles from 3 to 11 each.The expected(H_(e))and observed(H_(o))heterozygosities were 0.355–0.885 and 0.375–0.958,respectively.Most of the loci were highly polymorphic(polymorphism information content:0.316–0.852;mean:0.713),and these markers can be applied in the population genetic diversity research of H.nehereus.H_(o)wever,the transferability of these primers was low,probably because of the close relation of the collected species.In follow-up work,simple sequence repeats will be excavated with genome-based technologies,and related species will be gathered to address the present inadequacies.

An integrated microfluidics platform with high-throughput single-cell cloning array and concentration gradient generator for efficient cancer drug effect screening

Background:Tumor cell heterogeneity mediated drug resistance has been recognized as the stumbling block of cancer treatment.Elucidating the cytotoxicity of anticancer drugs at single-cell level in a high-throughput way is thus of great value for developing precision therapy.However,current techniques suffer from limitations in dynamically characterizing the responses of thousands of single cells or cell clones presented to multiple drug conditions.Methods:We developed a new microfluidics-based“SMART”platform that is Simple to operate,able to generate a Massive single-cell array and Multiplex drug concentrations,capable of keeping cells Alive,Retainable and Trackable in the microchambers.These features are achieved by integrating a Microfluidic chamber Array(4320 units)and a sixConcentration gradient generator(MAC),which enables highly efficient analysis of leukemia drug effects on single cells and cell clones in a high-throughput way.Results:A simple procedure produces 6 on-chip drug gradients to treat more than 3000 single cells or single-cell derived clones and thus allows an efficient and precise analysis of cell heterogeneity.The statistic results reveal that Imatinib(Ima)and Resveratrol(Res)combination treatment on single cells or clones is much more efficient than Ima or Res single drug treatment,indicated by the markedly reduced half maximal inhibitory concentration(IC50).Additionally,single-cell derived clones demonstrate a higher IC_(50) in each drug treatment compared to single cells.Moreover,primary cells isolated from two leukemia patients are also found with apparent heterogeneity upon drug treatment on MAC.Conclusions:This microfluidics-based“SMART”platform allows high-throughput single-cell capture and culture,dynamic drug-gradient treatment and cell response monitoring,which represents a new approach to efficiently investigate anticancer drug effects and should benefit drug discovery for leukemia and other cancers.

High-throughput mechanistic study of highly selective hydrogen-bonded organic frameworks for electrochemical nitrate reduction to ammonia

Hydrogen-bonded organic frameworks(HOFs),an emerging porous macrocyclic materials linked by hydrogen-bond,hold potential for gas separation and storage,sensors,optical,and electrocatalysts.Here,HOF-based electrocatalysts are rationally developed for nitrates reduction to ammonia,allowing not only to regulate wastewater pollution but also to accomplish carbon-neutral ammonia(NH_(3))synthesis.We preform high-throughput computational screening of thirty-six HOFs with various metals as active sites,denoted as HOF-M1,for nitrate reduction reaction(NO_(3)RR)toward NH_(3).We have implemented a hierarchical four-step screening strategy,and ultimately,HOF-Ti1 was selected based on its exceptional catalytic activity and selectivity in the NO_(3)RR process.Through additional analysis,we discovered that the d band center of the active metal sites serves as an effective parameter for designing and predicting the performance of HOFs in NO_(3)RR.This research not only showcases the immense potential of electrocatalysis in transforming NO_(3)RR into NH_(3)but also provides researchers with a compelling incentive to undertake further experimental investigations.

Opportunistic Routing with Multi-Channel Cooperative Neighbour Discovery

Due to the scattered nature of the network,data transmission in a dis-tributed Mobile Ad-hoc Network(MANET)consumes more energy resources(ER)than in a centralized network,resulting in a shorter network lifespan(NL).As a result,we build an Enhanced Opportunistic Routing(EORP)protocol architecture in order to address the issues raised before.This proposed routing protocol goal is to manage the routing cost by employing power,load,and delay to manage the routing energy consumption based on theflooding of control pack-ets from the target node.According to the goal of the proposed protocol techni-que,it is possible to manage the routing cost by applying power,load,and delay.The proposed technique also manage the routing energy consumption based on theflooding of control packets from the destination node in order to reduce the routing cost.Control packet exchange between the target and all the nodes,on the other hand,is capable of having an influence on the overall efficiency of the system.The EORP protocol and the Multi-channel Cooperative Neighbour Discovery(MCCND)protocol have been designed to detect the cooperative adja-cent nodes for each node in the routing route as part of the routing path discovery process,which occurs during control packet transmission.While control packet transmission is taking place during the routing path discovery process,the EORP protocol and the Multi-channel Cooperative Neighbour Discovery(MCCND)protocol have been designed to detect the cooperative adjacent nodes for each node in the routing.Also included is a simulation of these protocols in order to evaluate their performance across a wide range of packet speeds using Constant Bit Rate(CBR).When the packet rate of the CBR is 20 packets per second,the results reveal that the EORP-MCCND is 0.6 s quicker than the state-of-the-art protocols,according to thefindings.Assuming that the CBR packet rate is 20 packets per second,the EORP-MCCND achieves 0.6 s of End 2 End Delay,0.05 s of Routing Overhead Delay,120 s of Network Lifetime,and 20 J of Energy Consumption efficiency,which is much better than that of the state-of-the-art protocols.