In response to the shortcomings of the common encoders in the industry,of which the photoelectric encoders have a poor anti-interference ability in harsh industrial environments with water,oil,dust,or strong vibration...In response to the shortcomings of the common encoders in the industry,of which the photoelectric encoders have a poor anti-interference ability in harsh industrial environments with water,oil,dust,or strong vibrations and the magnetic encoders are too sensitive to magnetic field density,this paper designs a new differential encoder based on the grating eddy-current measurement principle,abbreviated as differential grating eddy-current encoder(DGECE).The grating eddy-current of DGECE consists of a circular array of trapezoidal reflection conductors and 16 trapezoidal coils with a special structure to form a differential relationship,which are respectively located on the code plate and the readout plate designed by a printed circuit board.The differential structure of DGECE corrects the common mode interference and the amplitude distortion due to the assembly to some extent,possesses a certain anti-interference capability,and greatly simplifies the regularization algorithm of the original data.By means of the corresponding readout circuit and demodulation algorithm,the DGECE can convert the periodic impedance variation of 16 coils into an angular output within the 360°cycle.Due to its simple manufacturing process and certain interference immunity,DGECE is easy to be integrated and mass-produced as well as applicable in the industrial spindles,especially in robot joints.This paper presents the measurement principle,implementation methods,and results of the experiment of the DGECE.The experimental results show that the accuracy of the DGECE can reach 0.237%and the measurement standard deviation can reach±0.14°within360°cycle.展开更多
In this paper,the sentiment classification method of multimodal adversarial autoencoder is studied.This paper includes the introduction of the multimodal adversarial autoencoder emotion classification method and the e...In this paper,the sentiment classification method of multimodal adversarial autoencoder is studied.This paper includes the introduction of the multimodal adversarial autoencoder emotion classification method and the experiment of the emotion classification method based on the encoder.The experimental analysis shows that the encoder has higher precision than other encoders in emotion classification.It is hoped that this analysis can provide some reference for the emotion classification under the current intelligent algorithm mode.展开更多
Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the st...Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks.TheWUSTL-EHMS 2020 dataset trains and evaluates themodel,constituting an imbalanced class distribution(87.46% normal traffic and 12.53% intrusion attacks).To address this imbalance,the study balances the effect of training Bias through Stratified K-fold cross-validation(K=5),so that each class is represented similarly on training and validation splits.Second,the Auto-Stack ID method combines many base classifiers such as TabNet,LightGBM,Gaussian Naive Bayes,Histogram-Based Gradient Boosting(HGB),and Logistic Regression.We apply a two-stage training process based on the first stage,where we have base classifiers that predict out-of-fold(OOF)predictions,which we use as inputs for the second-stage meta-learner XGBoost.The meta-learner learns to refine predictions to capture complicated interactions between base models,thus improving detection accuracy without introducing bias,overfitting,or requiring domain knowledge of the meta-data.In addition,the auto-stack ID model got 98.41% accuracy and 93.45%F1 score,better than individual classifiers.It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives.These findings identify its suitability in ensuring healthcare networks’security through ensemble learning.Ongoing efforts will be deployed in real time to improve response to evolving threats.展开更多
This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method u...This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data.These representations are then used as input for a support vector machine(SVM)-based metadata classifier,enabling precise detection of attack traffic.This architecture is designed to achieve both high detection accuracy and training efficiency,while adapting flexibly to the diverse service requirements and complexity of 5G network slicing.The model was evaluated using the DDoS Datasets 2022,collected in a simulated 5G slicing environment.Experiments were conducted under both class-balanced and class-imbalanced conditions.In the balanced setting,the model achieved an accuracy of 89.33%,an F1-score of 88.23%,and an Area Under the Curve(AUC)of 89.45%.In the imbalanced setting(attack:normal 7:3),the model maintained strong robustness,=achieving a recall of 100%and an F1-score of 90.91%,demonstrating its effectiveness in diverse real-world scenarios.Compared to existing AI-based detection methods,the proposed model showed higher precision,better handling of class imbalance,and strong generalization performance.Moreover,its modular structure is well-suited for deployment in containerized network function(NF)environments,making it a practical solution for real-world 5G infrastructure.These results highlight the potential of the proposed approach to enhance both the security and operational resilience of 5G slicing networks.展开更多
Rail surface damage is a critical component of high-speed railway infrastructure,directly affecting train operational stability and safety.Existing methods face limitations in accuracy and speed for small-sample,multi...Rail surface damage is a critical component of high-speed railway infrastructure,directly affecting train operational stability and safety.Existing methods face limitations in accuracy and speed for small-sample,multi-category,and multi-scale target segmentation tasks.To address these challenges,this paper proposes Pyramid-MixNet,an intelligent segmentation model for high-speed rail surface damage,leveraging dataset construction and expansion alongside a feature pyramid-based encoder-decoder network with multi-attention mechanisms.The encoding net-work integrates Spatial Reduction Masked Multi-Head Attention(SRMMHA)to enhance global feature extraction while reducing trainable parameters.The decoding network incorporates Mix-Attention(MA),enabling multi-scale structural understanding and cross-scale token group correlation learning.Experimental results demonstrate that the proposed method achieves 62.17%average segmentation accuracy,80.28%Damage Dice Coefficient,and 56.83 FPS,meeting real-time detection requirements.The model’s high accuracy and scene adaptability significantly improve the detection of small-scale and complex multi-scale rail damage,offering practical value for real-time monitoring in high-speed railway maintenance systems.展开更多
Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding ...Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.展开更多
Dynamic DNA nanotechnology plays a significant role in nanomedicine and information science due to its high programmability based on Watson-Crick base pairing and nanoscale dimensions.Intelligent DNA machines and netw...Dynamic DNA nanotechnology plays a significant role in nanomedicine and information science due to its high programmability based on Watson-Crick base pairing and nanoscale dimensions.Intelligent DNA machines and networks have been widely used in various fields,including molecular imaging,biosensors,drug delivery,information processing,and logic operations.Encoders serve as crucial components for information compilation and transfer,allowing the conversion of information from diverse application scenarios into a format recognized and applied by DNA circuits.However,there are only a few encoder designs with DNA outputs.Moreover,the molecular priority encoder is hardly designed.In this study,we introduce allosteric DNAzyme-based encoders for information transfer.The design of the allosteric domain and the recognition arm allows the input and output to be independent of each other and freely programmable.The pre-packaged mode design achieves uniformity of baseline dynamics and dynamics controllability.We also integrated non-nucleic acid molecules into the encoder through the aptamer design of the allosteric domain.Furthermore,we developed the 2^(n)-n encoder and the EndoⅣ-assisted priority encoder inspired by immunoglobulin's molecular structure and effector patterns.To our knowledge,the proposed encoder is the first enzyme-free DNA encoder with DNA output,and the priority encoder is the first molecular priority encoder in the DNA reaction network.Our encoders avoid complex operations on a single molecule,and their simple structure facilitates their application in complex DNA circuits and biological scenarios.展开更多
Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of cr...Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage space.This limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile devices.To solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion.Firstly,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of images.In addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context information.Finally,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map.We evaluate our method on three public crack datasets:DeepCrack,CFD,and Crack500.Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight crack detectionmodel,the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M.This advancement also facilitates technical support for portable scene detection.展开更多
In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is di...In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited t...Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.展开更多
Quantum communication networks,such as quantum key distribution(QKD)networks,typically employ the measurement-resend mechanism between two users using quantum communication devices based on different quantum encoding ...Quantum communication networks,such as quantum key distribution(QKD)networks,typically employ the measurement-resend mechanism between two users using quantum communication devices based on different quantum encoding types.To achieve direct communication between the devices with different quantum encoding types,in this paper,we propose encoding conversion schemes between the polarization bases(rectilinear,diagonal and circular bases)and the time-bin phase bases(two phase bases and time-bin basis)and design the quantum encoding converters.The theoretical analysis of the encoding conversion schemes is given in detail,and the basis correspondence of encoding conversion and the property of bit flip are revealed.The conversion relationship between polarization bases and time-bin phase bases can be easily selected by controlling a phase shifter.Since no optical switches are used in our scheme,the converter can be operated with high speed.The converters can also be modularized,which may be utilized to realize miniaturization in the future.展开更多
Blockchain,as a distributed ledger,inherently possesses tamper-resistant capabilities,creating a natural channel for covert communication.However,the immutable nature of data storage might introduce challenges to comm...Blockchain,as a distributed ledger,inherently possesses tamper-resistant capabilities,creating a natural channel for covert communication.However,the immutable nature of data storage might introduce challenges to communication security.This study introduces a blockchain-based covert communication model utilizing dynamic Base-K encoding.The proposed encoding scheme utilizes the input address sequence to determine K to encode the secret message and determines the order of transactions based on K,thus ensuring effective concealment of the message.The dynamic encoding parameters enhance flexibility and address issues related to identical transaction amounts for the same secret message.Experimental results demonstrate that the proposed method maintains smooth communication and low susceptibility to tampering,achieving commendable concealment and embedding rates.展开更多
The rational configuration of built-in electric field(IEF)in heterogeneous materials can significantly optimize the band structure to accelerate the separation of photogenerated charge carriers.However,the strength mo...The rational configuration of built-in electric field(IEF)in heterogeneous materials can significantly optimize the band structure to accelerate the separation of photogenerated charge carriers.However,the strength modulation of IEF formed by various materials has an uncertain enhancing effect on the separation of photogenerated carriers.Herein,a mesoporous MIL-125(Ti)@BiOCl S-scheme heterojunction with controllable IEF is prepared by green photoreduction reaction to investigate the relationship between IEF,microstructure,and photocatalytic activity.Moreover,the corresponding results demonstrate the MIL-125(Ti)@BiOCl effectively regulates the IEF strength through controlling the concentration of ligand defects,thereby optimizing the band structure and improving the efficiency of photogenerated charge separation.The optimized IEF significantly enhances the photocatalytic degradation performance of mesoporous MIL-125(Ti)-3@BiOCl towards tetracycline,with a k value of 0.07 min^(–1),which are approximately 5.5 and 4.7 times greater than that of BiOCl(0.0127 min^(–1))and MIL-125(Ti)-3(0.015 min^(–1)).These findings provide a new pathway for regulating IEF within MOF-based heterojunctions,and offer new insights into the intrinsic correlations between defect structure,IEF,and photocatalytic activity.展开更多
The quest for sustainable energy solutions has intensified the need for efficient water electrolysis techniques,pivotal for hydrogen production.However,developing effective bifunctional electrocatalysts capable of dri...The quest for sustainable energy solutions has intensified the need for efficient water electrolysis techniques,pivotal for hydrogen production.However,developing effective bifunctional electrocatalysts capable of driving the hydrogen evolution reaction(HER)and oxygen evolution reaction(OER)remains a formidable challenge.Addressing this,we introduce a novel built-in electric field(BEF)strategy to synthesize NiCoP–Co nanoarrays directly on Ti_(3)C_(2)T_(x) MXene substrates(NiCoP–Co/MXene).This approach leverages a significant work function difference(ΔΦ),propelling these nanoarrays as adept bifunctional electrocatalysts for comprehensive water splitting.MXene,in this process,plays a dual role.It acts as a conductive support,enhancing the catalyst’s overall conductivity,and facilitates an effective charge transport pathway,ensuring efficient charge transfer.Our study reveals that the BEF induces an electric field at the interface,prompting charge transfer from Co to NiCoP.This transfer modulates asymmetric charge distributions,which intricately control intermediates’adsorption and desorption dynamics.Such regulation is crucial for enhancing the reaction kinetics of both HER and OER.Furthermore,under oxidative conditions,the NiCoP–Co/MXene catalyst undergoes a structural metamorphosis into Ni(Co)oxides/hydroxides/MXene,increasing OER performance.This research demonstrates the BEF’s role in fine-tuning interfacial charge redistribution and underscores its potential in crafting more sophisticated electrocatalytic designs.The insights gained here could pave the way for the next generation of electrocatalysis,with far-reaching implications for energy conversion and storage technologies.展开更多
Solvated zinc ions are prone to undergo desolvation at the electrode/electrolyte interfaces,and unstable H_(2)O molecules within the solvated sheaths tend to trigger hydrogen evolution reaction(HER),further accelerati...Solvated zinc ions are prone to undergo desolvation at the electrode/electrolyte interfaces,and unstable H_(2)O molecules within the solvated sheaths tend to trigger hydrogen evolution reaction(HER),further accelerating interfaces decay.Herein,we propose for the first time a novel strategy to enhance the interfacial stabilities by insitu dynamic reconstruction of weakly solvated Zn2þduring the desolvation processes at heterointerfaces.Theoretical calculations indicate that,due to built-in electric field effects(BEFs),the plating/stripping mechanism shifts from[Zn(H_(2)O)_(6)]_(2)þto[Zn(H_(2)O)_(5)(SO_(4))^(2-)]_(2)þwithout additional electrolyte additives,reducing the solvation ability of H_(2)O,enhancing the competitive coordination of SO_(4)^(2-),essentially eliminating the undesirable side effects of anodes.Hence,symmetric cells can operate stably for 3000 h(51.7-times increase in cycle life),and the full cells can operate stably for 5000 cycles(51.5-times increase in cycle life).This study provides valuable insights into the critical design of weakly solvated Zn^(2+) þand desolvation processes at heterointerfaces.展开更多
Constructing heterostructures and facilitating surface reconstruction are effective ways to obtain excellent catalysts for the oxygen evolution reaction(OER).Surface reconstruction is a dynamic process that is affecte...Constructing heterostructures and facilitating surface reconstruction are effective ways to obtain excellent catalysts for the oxygen evolution reaction(OER).Surface reconstruction is a dynamic process that is affected by the built-in electric field of the heterostructure.In this study,P/N co-doped carbon-coated NiCo/Ni-CoO heterostructure was prepared by in situ acid etching,aniline polymerization,and pyrolysis.This method can form a tightly connected heterogeneous interface.It was found that introducing P-O bonds in the carbon shell can increase its work function,thereby enhancing the built-in electric field between the carbon shell and the core catalyst.Detailed characterizations confirm that the P-O bridge at the heterogeneous interface can provide an electron flow highway from the core to the shell.The generated carbon defects generated by P leaching during surface reconstruction also have strong electronabsorbing capacity.These effects promote the conversion of Co^(2+)to Co^(3+),thereby providing more highly active sites.The resulting catalyst shows significantly enhanced activity and stability.This study demonstrates the promoting effect of the built-in electric field on the surface reconstruction of the catalyst and emphasizes the importance of the construction of tightly connected heterogeneous interface,which is instructive for the design of excellent OER catalysts.展开更多
High-Speed Trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal im...High-Speed Trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal importance. As critical rotating mechanical components of the transmission system, bearings make their fault diagnosis a topic of extensive attention. This paper provides a systematic review of image encoding-based bearing fault diagnosis methods tailored to the condition monitoring of HSTs. First, it categorizes the image encoding techniques applied in the field of bearing fault diagnosis. Then, a review of state-of-the-art studies has been presented, encompassing both monomodal image conversion and multimodal image fusion approaches. Finally, it highlights current challenges and proposes future research directions to advance intelligent fault diagnosis in HSTs, aiming to provide a valuable reference for researchers and engineers in the field of intelligent operation and maintenance.展开更多
Rational design of defected carbons adjacent to nitrogen(N)dopants is a fascinating but challenging approach for enhancing the catalytic performance of N-doped carbon.Meanwhile,the combined effect of heteroatom doping...Rational design of defected carbons adjacent to nitrogen(N)dopants is a fascinating but challenging approach for enhancing the catalytic performance of N-doped carbon.Meanwhile,the combined effect of heteroatom doping and defect engineering can efficiently increase the oxygen reduction reaction(ORR)ability of inactive carbons through charge redistribution.Herein,we report that an enhanced built-in electric field caused by the combined effect of N-doping and carbon defects in the twodimensional(2D)mesoporous N-doped carbon nano flakes(NCNF)is a promising technique for improving ORR performance.As a result,the NCNF exhibits more promising ORR activity than Pt/C and similar performance with reported robust catalysts.Comprehensive experimental and theoretical investigations suggest that topologically defected carbon adjacent to the graphitic valley nitrogen is a real active site,rendering optimal energy for the adsorption of ORR intermediates and lowering the total energy barrier for ORR.Also,NCNF-based Zn-air batteries exhibited an excellent power density and specific capacity of~121.10 mW cm^(-2)and~679.86 mA h g_(Zn)^(-1),respectively.This study not only offers new insights into defected carbons with graphitic valley N for ORR but also proposes novel catalyst design principles and provides a solid grasp of the built-in electric field effect on the ORR performance of defective catalysts.展开更多
基金the Biomedical Science and Technology Support Special Project of Shanghai Science and Technology Committee(No.20S31908300)。
文摘In response to the shortcomings of the common encoders in the industry,of which the photoelectric encoders have a poor anti-interference ability in harsh industrial environments with water,oil,dust,or strong vibrations and the magnetic encoders are too sensitive to magnetic field density,this paper designs a new differential encoder based on the grating eddy-current measurement principle,abbreviated as differential grating eddy-current encoder(DGECE).The grating eddy-current of DGECE consists of a circular array of trapezoidal reflection conductors and 16 trapezoidal coils with a special structure to form a differential relationship,which are respectively located on the code plate and the readout plate designed by a printed circuit board.The differential structure of DGECE corrects the common mode interference and the amplitude distortion due to the assembly to some extent,possesses a certain anti-interference capability,and greatly simplifies the regularization algorithm of the original data.By means of the corresponding readout circuit and demodulation algorithm,the DGECE can convert the periodic impedance variation of 16 coils into an angular output within the 360°cycle.Due to its simple manufacturing process and certain interference immunity,DGECE is easy to be integrated and mass-produced as well as applicable in the industrial spindles,especially in robot joints.This paper presents the measurement principle,implementation methods,and results of the experiment of the DGECE.The experimental results show that the accuracy of the DGECE can reach 0.237%and the measurement standard deviation can reach±0.14°within360°cycle.
文摘In this paper,the sentiment classification method of multimodal adversarial autoencoder is studied.This paper includes the introduction of the multimodal adversarial autoencoder emotion classification method and the experiment of the emotion classification method based on the encoder.The experimental analysis shows that the encoder has higher precision than other encoders in emotion classification.It is hoped that this analysis can provide some reference for the emotion classification under the current intelligent algorithm mode.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R319),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publicationResearchers Supporting Project Number(RSPD2025R1107),King Saud University,Riyadh,Saudi Arabia.
文摘Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information.The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks.TheWUSTL-EHMS 2020 dataset trains and evaluates themodel,constituting an imbalanced class distribution(87.46% normal traffic and 12.53% intrusion attacks).To address this imbalance,the study balances the effect of training Bias through Stratified K-fold cross-validation(K=5),so that each class is represented similarly on training and validation splits.Second,the Auto-Stack ID method combines many base classifiers such as TabNet,LightGBM,Gaussian Naive Bayes,Histogram-Based Gradient Boosting(HGB),and Logistic Regression.We apply a two-stage training process based on the first stage,where we have base classifiers that predict out-of-fold(OOF)predictions,which we use as inputs for the second-stage meta-learner XGBoost.The meta-learner learns to refine predictions to capture complicated interactions between base models,thus improving detection accuracy without introducing bias,overfitting,or requiring domain knowledge of the meta-data.In addition,the auto-stack ID model got 98.41% accuracy and 93.45%F1 score,better than individual classifiers.It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives.These findings identify its suitability in ensuring healthcare networks’security through ensemble learning.Ongoing efforts will be deployed in real time to improve response to evolving threats.
基金supported by an Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(RS-2024-00438156,Development of Security Resilience Technology Based on Network Slicing Services in a 5G Specialized Network).
文摘This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data.These representations are then used as input for a support vector machine(SVM)-based metadata classifier,enabling precise detection of attack traffic.This architecture is designed to achieve both high detection accuracy and training efficiency,while adapting flexibly to the diverse service requirements and complexity of 5G network slicing.The model was evaluated using the DDoS Datasets 2022,collected in a simulated 5G slicing environment.Experiments were conducted under both class-balanced and class-imbalanced conditions.In the balanced setting,the model achieved an accuracy of 89.33%,an F1-score of 88.23%,and an Area Under the Curve(AUC)of 89.45%.In the imbalanced setting(attack:normal 7:3),the model maintained strong robustness,=achieving a recall of 100%and an F1-score of 90.91%,demonstrating its effectiveness in diverse real-world scenarios.Compared to existing AI-based detection methods,the proposed model showed higher precision,better handling of class imbalance,and strong generalization performance.Moreover,its modular structure is well-suited for deployment in containerized network function(NF)environments,making it a practical solution for real-world 5G infrastructure.These results highlight the potential of the proposed approach to enhance both the security and operational resilience of 5G slicing networks.
基金supported in part by the National Natural Science Foundation of China under Grant 6226070954Jiangxi Provincial Key R&D Programme under Grant 20244BBG73002.
文摘Rail surface damage is a critical component of high-speed railway infrastructure,directly affecting train operational stability and safety.Existing methods face limitations in accuracy and speed for small-sample,multi-category,and multi-scale target segmentation tasks.To address these challenges,this paper proposes Pyramid-MixNet,an intelligent segmentation model for high-speed rail surface damage,leveraging dataset construction and expansion alongside a feature pyramid-based encoder-decoder network with multi-attention mechanisms.The encoding net-work integrates Spatial Reduction Masked Multi-Head Attention(SRMMHA)to enhance global feature extraction while reducing trainable parameters.The decoding network incorporates Mix-Attention(MA),enabling multi-scale structural understanding and cross-scale token group correlation learning.Experimental results demonstrate that the proposed method achieves 62.17%average segmentation accuracy,80.28%Damage Dice Coefficient,and 56.83 FPS,meeting real-time detection requirements.The model’s high accuracy and scene adaptability significantly improve the detection of small-scale and complex multi-scale rail damage,offering practical value for real-time monitoring in high-speed railway maintenance systems.
基金supported by Natural Science Foundation Programme of Gansu Province(No.24JRRA231)National Natural Science Foundation of China(No.62061023)Gansu Provincial Science and Technology Plan Key Research and Development Program Project(No.24YFFA024).
文摘Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.
基金financially supported by the National Natural Science Foundation of China(No.82172372)the Opening Research Fund of State Key Laboratory of Digital Medical Engineering(No.2023-M04)。
文摘Dynamic DNA nanotechnology plays a significant role in nanomedicine and information science due to its high programmability based on Watson-Crick base pairing and nanoscale dimensions.Intelligent DNA machines and networks have been widely used in various fields,including molecular imaging,biosensors,drug delivery,information processing,and logic operations.Encoders serve as crucial components for information compilation and transfer,allowing the conversion of information from diverse application scenarios into a format recognized and applied by DNA circuits.However,there are only a few encoder designs with DNA outputs.Moreover,the molecular priority encoder is hardly designed.In this study,we introduce allosteric DNAzyme-based encoders for information transfer.The design of the allosteric domain and the recognition arm allows the input and output to be independent of each other and freely programmable.The pre-packaged mode design achieves uniformity of baseline dynamics and dynamics controllability.We also integrated non-nucleic acid molecules into the encoder through the aptamer design of the allosteric domain.Furthermore,we developed the 2^(n)-n encoder and the EndoⅣ-assisted priority encoder inspired by immunoglobulin's molecular structure and effector patterns.To our knowledge,the proposed encoder is the first enzyme-free DNA encoder with DNA output,and the priority encoder is the first molecular priority encoder in the DNA reaction network.Our encoders avoid complex operations on a single molecule,and their simple structure facilitates their application in complex DNA circuits and biological scenarios.
基金supported by the National Natural Science Foundation of China(No.62176034)the Science and Technology Research Program of Chongqing Municipal Education Commission(No.KJZD-M202300604)the Natural Science Foundation of Chongqing(Nos.cstc2021jcyj-msxmX0518,2023NSCQ-MSX1781).
文摘Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage space.This limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile devices.To solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion.Firstly,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of images.In addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context information.Finally,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map.We evaluate our method on three public crack datasets:DeepCrack,CFD,and Crack500.Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight crack detectionmodel,the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M.This advancement also facilitates technical support for portable scene detection.
文摘In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the Remaining Useful Life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
文摘Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.
基金supported by the National Natural Science Foundation of China(Grant No.62001440).
文摘Quantum communication networks,such as quantum key distribution(QKD)networks,typically employ the measurement-resend mechanism between two users using quantum communication devices based on different quantum encoding types.To achieve direct communication between the devices with different quantum encoding types,in this paper,we propose encoding conversion schemes between the polarization bases(rectilinear,diagonal and circular bases)and the time-bin phase bases(two phase bases and time-bin basis)and design the quantum encoding converters.The theoretical analysis of the encoding conversion schemes is given in detail,and the basis correspondence of encoding conversion and the property of bit flip are revealed.The conversion relationship between polarization bases and time-bin phase bases can be easily selected by controlling a phase shifter.Since no optical switches are used in our scheme,the converter can be operated with high speed.The converters can also be modularized,which may be utilized to realize miniaturization in the future.
基金sponsored by the National Natural Science Foundation of China No.U24B201114,6247070859,62302114 and No.62172353Innovation Fund Program of the Engineering Research Center for Integration and Application of Digital Learning Technology of Ministry of Education No.1331007 and No.1311022Natural Science Foundation of Guangdong Province No.2024A1515010177.
文摘Blockchain,as a distributed ledger,inherently possesses tamper-resistant capabilities,creating a natural channel for covert communication.However,the immutable nature of data storage might introduce challenges to communication security.This study introduces a blockchain-based covert communication model utilizing dynamic Base-K encoding.The proposed encoding scheme utilizes the input address sequence to determine K to encode the secret message and determines the order of transactions based on K,thus ensuring effective concealment of the message.The dynamic encoding parameters enhance flexibility and address issues related to identical transaction amounts for the same secret message.Experimental results demonstrate that the proposed method maintains smooth communication and low susceptibility to tampering,achieving commendable concealment and embedding rates.
文摘The rational configuration of built-in electric field(IEF)in heterogeneous materials can significantly optimize the band structure to accelerate the separation of photogenerated charge carriers.However,the strength modulation of IEF formed by various materials has an uncertain enhancing effect on the separation of photogenerated carriers.Herein,a mesoporous MIL-125(Ti)@BiOCl S-scheme heterojunction with controllable IEF is prepared by green photoreduction reaction to investigate the relationship between IEF,microstructure,and photocatalytic activity.Moreover,the corresponding results demonstrate the MIL-125(Ti)@BiOCl effectively regulates the IEF strength through controlling the concentration of ligand defects,thereby optimizing the band structure and improving the efficiency of photogenerated charge separation.The optimized IEF significantly enhances the photocatalytic degradation performance of mesoporous MIL-125(Ti)-3@BiOCl towards tetracycline,with a k value of 0.07 min^(–1),which are approximately 5.5 and 4.7 times greater than that of BiOCl(0.0127 min^(–1))and MIL-125(Ti)-3(0.015 min^(–1)).These findings provide a new pathway for regulating IEF within MOF-based heterojunctions,and offer new insights into the intrinsic correlations between defect structure,IEF,and photocatalytic activity.
基金supported by Guangdong Basic and Applied Basic Research Foundation(Nos.2021A1515010261 and 2023A1515140153)Guangdong Special Innovative Projects of General Universities(No.2022KTSCX136)+1 种基金the Major and Special Project in the Field of Intelligent Manufacturing of the Universities in Guangdong Province(No.2020ZDZX2067)the Innovative Team Project of the Universities in Guangdong Province(No.2023KCXTD035).
文摘The quest for sustainable energy solutions has intensified the need for efficient water electrolysis techniques,pivotal for hydrogen production.However,developing effective bifunctional electrocatalysts capable of driving the hydrogen evolution reaction(HER)and oxygen evolution reaction(OER)remains a formidable challenge.Addressing this,we introduce a novel built-in electric field(BEF)strategy to synthesize NiCoP–Co nanoarrays directly on Ti_(3)C_(2)T_(x) MXene substrates(NiCoP–Co/MXene).This approach leverages a significant work function difference(ΔΦ),propelling these nanoarrays as adept bifunctional electrocatalysts for comprehensive water splitting.MXene,in this process,plays a dual role.It acts as a conductive support,enhancing the catalyst’s overall conductivity,and facilitates an effective charge transport pathway,ensuring efficient charge transfer.Our study reveals that the BEF induces an electric field at the interface,prompting charge transfer from Co to NiCoP.This transfer modulates asymmetric charge distributions,which intricately control intermediates’adsorption and desorption dynamics.Such regulation is crucial for enhancing the reaction kinetics of both HER and OER.Furthermore,under oxidative conditions,the NiCoP–Co/MXene catalyst undergoes a structural metamorphosis into Ni(Co)oxides/hydroxides/MXene,increasing OER performance.This research demonstrates the BEF’s role in fine-tuning interfacial charge redistribution and underscores its potential in crafting more sophisticated electrocatalytic designs.The insights gained here could pave the way for the next generation of electrocatalysis,with far-reaching implications for energy conversion and storage technologies.
基金financially supported by the National Natural Science Foundation of China(51977097).
文摘Solvated zinc ions are prone to undergo desolvation at the electrode/electrolyte interfaces,and unstable H_(2)O molecules within the solvated sheaths tend to trigger hydrogen evolution reaction(HER),further accelerating interfaces decay.Herein,we propose for the first time a novel strategy to enhance the interfacial stabilities by insitu dynamic reconstruction of weakly solvated Zn2þduring the desolvation processes at heterointerfaces.Theoretical calculations indicate that,due to built-in electric field effects(BEFs),the plating/stripping mechanism shifts from[Zn(H_(2)O)_(6)]_(2)þto[Zn(H_(2)O)_(5)(SO_(4))^(2-)]_(2)þwithout additional electrolyte additives,reducing the solvation ability of H_(2)O,enhancing the competitive coordination of SO_(4)^(2-),essentially eliminating the undesirable side effects of anodes.Hence,symmetric cells can operate stably for 3000 h(51.7-times increase in cycle life),and the full cells can operate stably for 5000 cycles(51.5-times increase in cycle life).This study provides valuable insights into the critical design of weakly solvated Zn^(2+) þand desolvation processes at heterointerfaces.
基金financially supported by the National Natural Science Foundation of China(Grant No.52073106)。
文摘Constructing heterostructures and facilitating surface reconstruction are effective ways to obtain excellent catalysts for the oxygen evolution reaction(OER).Surface reconstruction is a dynamic process that is affected by the built-in electric field of the heterostructure.In this study,P/N co-doped carbon-coated NiCo/Ni-CoO heterostructure was prepared by in situ acid etching,aniline polymerization,and pyrolysis.This method can form a tightly connected heterogeneous interface.It was found that introducing P-O bonds in the carbon shell can increase its work function,thereby enhancing the built-in electric field between the carbon shell and the core catalyst.Detailed characterizations confirm that the P-O bridge at the heterogeneous interface can provide an electron flow highway from the core to the shell.The generated carbon defects generated by P leaching during surface reconstruction also have strong electronabsorbing capacity.These effects promote the conversion of Co^(2+)to Co^(3+),thereby providing more highly active sites.The resulting catalyst shows significantly enhanced activity and stability.This study demonstrates the promoting effect of the built-in electric field on the surface reconstruction of the catalyst and emphasizes the importance of the construction of tightly connected heterogeneous interface,which is instructive for the design of excellent OER catalysts.
基金supported by the Fundamental Research Funds for the Central Universities(No.2024JBZX027)the National Natural Science Foundation of China(No.52375078).
文摘High-Speed Trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal importance. As critical rotating mechanical components of the transmission system, bearings make their fault diagnosis a topic of extensive attention. This paper provides a systematic review of image encoding-based bearing fault diagnosis methods tailored to the condition monitoring of HSTs. First, it categorizes the image encoding techniques applied in the field of bearing fault diagnosis. Then, a review of state-of-the-art studies has been presented, encompassing both monomodal image conversion and multimodal image fusion approaches. Finally, it highlights current challenges and proposes future research directions to advance intelligent fault diagnosis in HSTs, aiming to provide a valuable reference for researchers and engineers in the field of intelligent operation and maintenance.
基金supported by the National Natural Science Foundation of China(22262010,22062005,22165005,U20A20128)Guangxi Science and Technology Fund for Distinguished HighTalent Introduction Program(AC22035091)Guangxi Science Fund for Distinguished Young Scholars(2019GXNSFFA245016)。
文摘Rational design of defected carbons adjacent to nitrogen(N)dopants is a fascinating but challenging approach for enhancing the catalytic performance of N-doped carbon.Meanwhile,the combined effect of heteroatom doping and defect engineering can efficiently increase the oxygen reduction reaction(ORR)ability of inactive carbons through charge redistribution.Herein,we report that an enhanced built-in electric field caused by the combined effect of N-doping and carbon defects in the twodimensional(2D)mesoporous N-doped carbon nano flakes(NCNF)is a promising technique for improving ORR performance.As a result,the NCNF exhibits more promising ORR activity than Pt/C and similar performance with reported robust catalysts.Comprehensive experimental and theoretical investigations suggest that topologically defected carbon adjacent to the graphitic valley nitrogen is a real active site,rendering optimal energy for the adsorption of ORR intermediates and lowering the total energy barrier for ORR.Also,NCNF-based Zn-air batteries exhibited an excellent power density and specific capacity of~121.10 mW cm^(-2)and~679.86 mA h g_(Zn)^(-1),respectively.This study not only offers new insights into defected carbons with graphitic valley N for ORR but also proposes novel catalyst design principles and provides a solid grasp of the built-in electric field effect on the ORR performance of defective catalysts.
