Structural properties of residual carbon in coal gasification fine slag and their influence on flotation separation and resource utilization:A review

Coal gasification fine slag(FS)is a typical solid waste generated in coal gasification.Its current disposal methods of stockpil-ing and landfilling have caused serious soil and ecological hazards.Separation recovery and the high-value utilization of residual carbon(RC)in FS are the keys to realizing the win-win situation of the coal chemical industry in terms of economic and environmental benefits.The structural properties,such as pore,surface functional group,and microcrystalline structures,of RC in FS(FS-RC)not only affect the flotation recovery efficiency of FS-RC but also form the basis for the high-value utilization of FS-RC.In this paper,the characteristics of FS-RC in terms of pore structure,surface functional groups,and microcrystalline structure are sorted out in accordance with gasification type and FS particle size.The reasons for the formation of the special structural properties of FS-RC are analyzed,and their influence on the flotation separation and high-value utilization of FS-RC is summarized.Separation methods based on the pore structural characterist-ics of FS-RC,such as ultrasonic pretreatment-pore-blocking flotation and pore breaking-flocculation flotation,are proposed to be the key development technologies for improving FS-RC recovery in the future.The design of low-cost,low-dose collectors containing polar bonds based on the surface and microcrystalline structures of FS-RC is proposed to be an important breakthrough point for strengthening the flotation efficiency of FS-RC in the future.The high-value utilization of FS should be based on the physicochemical structural proper-ties of FS-RC and should focus on the environmental impact of hazardous elements and the recyclability of chemical waste liquid to es-tablish an environmentally friendly utilization method.This review is of great theoretical importance for the comprehensive understand-ing of the unique structural properties of FS-RC,the breakthrough of the technological bottleneck in the efficient flotation separation of FS,and the expansion of the field of the high value-added utilization of FS-RC.

Effect of quenching cooling rate on residual stress and microstructure evolution of 6061 aluminum alloy

In this study,the cooling rate was manipulated by quenching with water of different temperatures(30,60 and 100℃).Surface and internal residual stresses in the quenched 6061 aluminum alloy samples were measured using hole-drilling and crack compliance methods,respectively.Then,the processability of the quenched samples was evaluated at cryogenic temperatures.The mechanical properties of the as-aged samples were assessed,and microstructure evolution was analyzed.The surface residual stresses of samples W30℃,W60℃and W100℃is−178.7,−161.7 and−117.2 MPa,respectively along x-direction,respectively;and−191.2,−172.1 and−126.2 MPa,respectively along y-direction.The sample quenched in boiling water displaying the lowest residual stress(~34%and~60%reduction in the surface and core).The generation and distribution of quenching residual stress could be attributed to the lattice distortion gradient.Desirable plasticity was also exhibited in the samples with relatively low quenching cooling rates at cryogenic temperatures.The strengthes of the as-aged samples are 291.2 to 270.1 MPa as the quenching water temperature increase from 30℃to 100℃.Fine and homogeneous β"phases were observed in the as-aged sample quenched with boiling water due to the clusters and Guinier-Preston zones(GP zones)premature precipitated during quenching process.

Residual alkali-evoked cross-linked polymer layer for anti-air-sensitivity LiNi_(0.89)Co_(0.06)Mn_(0.05)O_(2)cathode

High-energy density lithium-ion batteries(LIBs)with layered high-nickel oxide cathodes(LiNi_(x)Co_(y)Mn_(1-x-y)O_(2),x≥0.8)show great promise in consumer electronics and vehicular applications.However,LiNi_(x)Co_(y)Mn_(1-x-y)O_(2)faces challenges related to capacity decay caused by residual alkalis owing to high sensitivity to air.To address this issue,we propose a hazardous substances upcycling method that fundamentally mitigates alkali content and concurrently induces the emergence of an anti-air-sensitive layer on the cathode surface.Through the neutralization of polyacrylic acid(PAA)with residual alkalis and then coupling it with 3-aminopropyl triethoxysilane(KH550),a stable and ion-conductive cross-linked polymer layer is in situ integrated into the LiNi_(0.89)Co_(0.06)Mn_(0.05)O_(2)(NCM)cathode.Our characterization and measurements demonstrate its effectiveness.The NCM material exhibits impressive cycling performance,retaining 88.4%of its capacity after 200 cycles at 5 C and achieving an extraordinary specific capacity of 170.0 mA h g^(-1) at 10 C.Importantly,this layer on the NCM efficiently suppresses unfavorable phase transitions,severe electrolyte degradation,and CO_(2)gas evolution,while maintaining commendable resistance to air exposure.This surface modification strategy shows widespread potential for creating air-stable LiNi_(x)Co_(y)Mn_(1-x-y)O_(2)cathodes,thereby advancing high-performance LIBs.

Effects of soil crust on the collapsing erosion of colluvial deposits with granite residual soil

Collapsing erosion is a unique phenomenon commonly observed on the granite residue hillslopes in the tropical and subtropical regions of southern China,characterized by its abrupt occurrence and significant erosion volumes.However,the impacts of soil crust conditions on the erosion of colluvial deposits with granite residual soils have only been studied to a limited extent.To address this issue,this study investigates the impacts of three soil crust conditions(i.e.,without crust,10-minute crust,and 20-minute crust)on gully morphology,rainfall infiltration,and runoff and sediment yield during slope erosion of colluvial deposits with granite residues(classified as Acrisols)in Yudu County,Ganzhou City,Jiangxi Province,China,using simulated rainfall tests and photographic methods.The results showed that as the strength of the soil crust increased,the capacity of moisture infiltration and the width and depth of the gully as well as the sediment concentration and yield ratio decreased;at the same time,the runoff ratio increased.The sediment yield in the without-crust test was found to be 1.24 and 1.43 times higher than that observed in the 10-minute crust and 20-minute crust tests,respectively.These results indicate that soil crusts can effectively prevent slope erosion and moisture infiltration,while providing valuable insights for the management of soil erosion in natural environments.

A modified smoothed particle hydrodynamics method considering residual stress for simulating failure and its application in layered rock mass

Residual strength is an indispensable factor in evaluating rock fracture,yet the current Smoothed Particle Hydrodynamics(SPH)framework rarely considers its influence when simulating fracture.An improved cracking strategy considering residual stress in the base bond SPH method was proposed to simulate failures in layered rocks and slopes and verified by experimental results and other simulation methods(i.e.,the discrete element method).Modified Mohr–Coulomb failure criterion was applied to distinguish the mixed failure of tensile and shear.Bond fracture markψwas introduced to improve the kernel function after tensile damage,and the calculation of residual stress after the damage was derived after shear damage.Numerical simulations were carried out to evaluate its performance under different stress and scale conditions and to verify its effectiveness in realistically reproducing crack initiation and propagation and coalescence,even fracture and separation.The results indicate that the improved cracking strategy precisely captures the fracture and failure pattern in layered rocks and rock slopes.The residual stress of brittle tock is correctly captured by the improved SPH method.The improved SPH method that considers residual strength shows an approximately 13%improvement in accuracy for the safety factor of anti-dip layered slopes compared to the method that does not consider residual strength,as validated against analytical solutions.We infer that the improved SPH method is effective and shows promise for applications to continuous and discontinuous rock masses.

A method for establishing a bearing residual life prediction model for process enhancement equipment based on rotor imbalance response analysis

A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adhere to the wire mesh packing in the rotor,thus resulting in an imbalance in the vibration of the rotor,which may cause serious damage to the bearing and material leakage.This study proposes a model prediction for estimating the bearing residual life of a rotating packed bed based on rotor imbalance response analysis.This method is used to determine the influence of the mass on the imbalance in the vibration of the rotor on bearing damage.The major influence on rotor vibration was found to be exerted by the imbalanced mass and its distribution radius,as revealed by the results of orthogonal experiments.Through implementing finite element analysis,the imbalance response curve for the rotating packed bed rotor was obtained,and a correlation among rotor imbalance mass,distribution radius of imbalance mass,and bearing residue life was established via data fitting.The predicted value of the bearing life can be used as the reference basis for an early safety warning of a rotating packed bed to effectively avoid accidents.

CRISPR/Cas9-mediated NlInR2 mutants:Analyses of residual mRNA and truncated proteins

CRISPR/Cas9 technology is a powerful genome manipulation tool in insects.However,little is known about whether mRNA and protein of a target gene are completely cleared in homozygous mutants.This study generated homozygous mutants of the insulin receptor gene 2(NlInR2)in the brown planthopper(Nilaparvata lugens)using CRISPR/Cas9 genome editing.Both frameshift mutants,E5_D17 and E6_I7,differentiated towards long wings,but there were differences in wing morphology,with E5_D17 showing wing deformities.Subsequent investigations revealed the presence of residual expression of NlInR2 mRNA in both mutants,as well as the occurrence of spliceosomes featuring exon skipping splicing in E5_D17.Additionally,the E5_D17 exhibited the detection of N-terminally truncated NlInR2 protein.RNA interference experiments indicated that the knockdown of NlInR2 expression in the E5_D17 mutant line increased the proportion of wing deformities from 11.1 to 65.6%,suggesting that the residual NlInR2 mRNA of the E5_D17 mutant might have retained some genetic functions.Our results imply that systematic characterization of residual protein expression or function in CRISPR/Cas9-generated mutant lines is necessary for phenotypic interpretation.

Residual stress modeling of mitigated fused silica damage sites with CO_(2)laser annealing

A numerical model based on measured fictive temperature distributions is explored to evaluate the residual stress fields of CO_(2)laser-annealed mitigated fused silica damage sites.The proposed model extracts the residual strain from the differences in thermoelastic contraction of fused silica with different fictive temperatures from the initial frozen-in temperatures to ambient temperature.The residual stress fields of mitigated damage sites for the CO_(2)laser-annealed case are obtained by a finite element analysis of equilibrium equations and constitutive equations.The simulated results indicate that the proposed model can accurately evaluate the residual stress fields of laser-annealed mitigated damage sites with a complex thermal history.The calculated maximum hoop stress is in good agreement with the reported experimental result.The estimated optical retardance profiles from the calculated radial and hoop stress fields are consistent with the photoelastic measurements.These results provide sufficient evidence to demonstrate the suitability of the proposed model for describing the residual stresses of mitigated fused silica damage sites after CO_(2)laser annealing.

Molecular simulation study on the evolution process of hydrate residual structures into hydrate

The clathrate hydrate memory effect is a fascinating phenomenon with potential applications in carbon capture,utilization and storage(CCUS),gas separation,and gas storage as it can accelerate the secondary formation of clathrate hydrate.However,the underlying mechanism of this effect remains unclear.To gain a better understanding of the mechanism,we conducted molecular dynamic simulations to simulate the initial formation and reformation processes of methane hydrate.In this work,we showed the evolution process of hydrate residual structures into hydrate cages.The simulation results indicate that the residual structures are closely related to the existence of hydrate memory effect,and the higher the contribution of hydrate dissociated water to the hydrate nucleation process,the faster the hydrate nucleation.After hydrate dissociation,the locally ordered structures still exist after hydrate dissociation and can promote the formation of cluster structures,thus accelerating hydrate nucleation.Additionally,the nucleation process of hydrate and the formation process of clusters are inseparable.The size of clusters composed of cup-cage structures is critical for hydrate nucleation.The residence time at high temperature after hydrate decomposition will affect the strength of the hydrate memory effect.Our simulation results provide microscopic insights into the occurrence of the hydrate memory effect and shed light on the hydrate reformation process at the molecular scale.

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

Residual subsidence time series model in mountain area caused by underground mining based on GNSS online monitoring

The residual subsidence caused by underground mining in mountain area has a long subsidence duration time and great potential harm,which seriously threatens the safety of people's production and life in the mining area.Therefore,it is necessary to use appropriate monitoring methods and mathematical models to effectively monitor and predict the residual subsidence caused by underground mining.Compared with traditional level survey and InSAR(Interferometric Synthetic Aperture Radar)technology,GNSS(Global Navigation Satellite System)online monitoring technology has the advantages of long-term monitoring,high precision and more flexible monitoring methods.The empirical equation method of residual subsidence in mining subsidence is effectively combined with the rock creep equation,which can not only describe the residual subsidence process from the mechanism,but also predict the residual subsidence.Therefore,based on GNSS online monitoring technology,combined with the mining subsidence model of mountain area and adding the correlation coefficient of the compaction degree of caving broken rock and the Kelvin model of rock mechanics,this paper constructs the residual subsidence time series model of arbitrary point on the ground in mountain area.Through the example,the predicted results of the model in the inversion parameter phase and the dynamic prediction phase are compared with the measured data sequence.The results show that the model can carry out effective numerical calculation according to the GNSS monitoring data of any point on the ground,and the model prediction effect is good,which provides a new method for the prediction of residual subsidence in mountain mining.

Antagonism effect of residual S triggers the dual-path mechanism for water oxidation

Transition metal chalcogenides(TMCs)are recognized as pre-catalysts,and their(oxy)hydroxides derived from electrochemical reconstruction are the active species in the water oxidation.However,understanding the role of the residual chalcogen in the reconstructed layer is lacking in detail,and the corresponding catalytic mechanism remains controversial.Here,taking Cu_(1-x)Co_(x)S as a platform,we explore the regulating effect and existence form of the residual S doped into the reconstructive layer for oxygen evolution reaction(OER),where a dual-path OER mechanism is proposed.First-principles calculations and operando~(18)O isotopic labeling experiments jointly reveal that the residual S in the reconstructive layer of Cu_(1-x)Co_(x)S can wisely balance the adsorbate evolution mechanism(AEM)and lattice oxygen oxidation mechanism(LOM)by activating lattice oxygen and optimizing the adsorption/desorption behaviors at metal active sites,rather than change the reaction mechanism from AEM to LOM.Following such a dual-path OER mechanism,Cu_(0.4)Co_(0.6)S-derived Cu_(0.4)Co_(0.6)OSH not only overcomes the restriction of linear scaling relationship in AEM,but also avoids the structural collapse caused by lattice oxygen migration in LOM,so as to greatly reduce the OER potential and improved stability.

Attention-Based Residual Dense Shrinkage Network for ECG Denoising

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac diagnosis.However,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological analysis.Therefore,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic research.Aiming at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this paper.Firstly,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise suppression.Finally,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG signal.Experiments on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

Direct scaling of residual displacements for bilinear and pinching oscillators

The estimation of residual displacements in a structure due to an anticipated earthquake event has increasingly become an important component of performance-based earthquake engineering because controlling these displacements plays an important role in ensuring cost-feasible or cost-effective repairs in a damaged structure after the event.An attempt is made in this study to obtain statistical estimates of constant-ductility residual displacement spectra for bilinear and pinching oscillators with 5%initial damping,directly in terms of easily available seismological,site,and model parameters.None of the available models for the bilinear and pinching oscillators are useful when design spectra for a seismic hazard at a site are not available.The statistical estimates of a residual displacement spectrum are proposed in terms of earthquake magnitude,epicentral distance,site geology parameter,and three model parameters for a given set of ductility demand and a hysteretic energy capacity coefficient in the case of bilinear and pinching models,as well as for a given set of pinching parameters for displacement and strength at the breakpoint in the case of pinching model alone.The proposed scaling model is applicable to horizontal ground motions in the western U.S.for earthquake magnitudes less than 7 or epicentral distances greater than 20 km.

Quality assessment of the Topo-Iberia CGPS stations and data quality's effects on postfit ionosphere-free phase residuals

This paper analyzed GPS data from the Topo-Iberia network spanning almost 12 years(2008-2020).The data quality information for all 26 Topo-Iberia stations is provided for the first time,complementing the Spanish Geological Survey’s storage work.Data analyses based on quality indicators obtained using TEQC have been carried out.The guidelines and data quality information from the IGS stations have been considered as the quality references,with the stations ALJI,EPCU,and TIOU standing out as the worst stations,while on the contrary,FUEN,PALM,PILA,and TRIA meet the quality requirements to become an IGS station.The relationship between the GPS data quality and their GAMIT-and Gipsy X-derived postfit ionosphere-free phase residuals has also been investigated,and the results reveal an inversely proportional relationship.It has been found that the stations showing an increase in elevation of the horizon line,also show an increase in cycle slips and multipath,are among the poorest quality stations,and among those with the highest postfit RMS of phase residuals.Moreover,the evolution of the vegetation around the antenna should be considered as it could cause a progressive loss of quality,which is not complying with the IGS standards.The quality assessment shows that the Topo-Iberia stations are appropriate for geodetic purposes,but permanent monitoring would be necessary to avoid the least possible loss of data and quality.In addition,a method to characterize the GNSS data quality is proposed.

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more intelligent.IoT has been widely applied in various scenarios,including urban infrastructure,transportation,industry,personal life,and other socio-economic fields.The introduction of deep learning has brought new security challenges,like an increment in abnormal traffic,which threatens network security.Insufficient feature extraction leads to less accurate classification results.In abnormal traffic detection,the data of network traffic is high-dimensional and complex.This data not only increases the computational burden of model training but also makes information extraction more difficult.To address these issues,this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic detection.To fully utilize the multi-scale information in network traffic,a Multi-scale Dilated feature extraction(MD)block is introduced.This module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model’s receptive field.The proposed Max-feature-map Residual with Dual-channel pooling(MRD)block integrates the maximum feature map with the residual block.This module ensures the model focuses on key information,thereby optimizing computational efficiency and reducing unnecessary information redundancy.Experimental results show that compared to the latest methods,the proposed abnormal traffic detection model improves accuracy by about 2%.

Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network

Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.

A Deep Residual PLS for Data-Driven Quality Prediction Modeling in Industrial Process

Partial least squares(PLS)model is the most typical data-driven method for quality-related industrial tasks like soft sensor.However,only linear relations are captured between the input and output data in the PLS.It is difficult to obtain the remaining nonlinear information in the residual subspaces,which may deteriorate the prediction performance in complex industrial processes.To fully utilize data information in PLS residual subspaces,a deep residual PLS(DRPLS)framework is proposed for quality prediction in this paper.Inspired by deep learning,DRPLS is designed by stacking a number of PLSs successively,in which the input residuals of the previous PLS are used as the layer connection.To enhance representation,nonlinear function is applied to the input residuals before using them for stacking highlevel PLS.For each PLS,the output parts are just the output residuals from its previous PLS.Finally,the output prediction is obtained by adding the results of each PLS.The effectiveness of the proposed DRPLS is validated on an industrial hydrocracking process.

Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.