Decade Milestone Advancement of Defect-Engineered g-C_(3)N_(4) for Solar Catalytic Applications

Over the past decade, graphitic carbon nitride(g-C_(3)N_(4)) has emerged as a universal photocatalyst toward various sustainable carbo-neutral technologies. Despite solar applications discrepancy, g-C_(3)N_(4) is still confronted with a general fatal issue of insufficient supply of thermodynamically active photocarriers due to its inferior solar harvesting ability and sluggish charge transfer dynamics. Fortunately, this could be significantly alleviated by the “all-in-one” defect engineering strategy, which enables a simultaneous amelioration of both textural uniqueness and intrinsic electronic band structures. To this end, we have summarized an unprecedently comprehensive discussion on defect controls including the vacancy/non-metallic dopant creation with optimized electronic band structure and electronic density, metallic doping with ultraactive coordinated environment(M–N_(x), M–C_(2)N_(2), M–O bonding), functional group grafting with optimized band structure, and promoted crystallinity with extended conjugation π system with weakened interlayered van der Waals interaction. Among them, the defect states induced by various defect types such as N vacancy, P/S/halogen dopants, and cyano group in boosting solar harvesting and accelerating photocarrier transfer have also been emphasized. More importantly, the shallow defect traps identified by femtosecond transient absorption spectra(fs-TAS) have also been highlighted. It is believed that this review would pave the way for future readers with a unique insight into a more precise defective g-C_(3)N_(4) “customization”, motivating more profound thinking and flourishing research outputs on g-C_(3)N_(4)-based photocatalysis.

Heterointerface Engineering-Induced Oxygen Defects for the Manganese Dissolution Inhibition in Aqueous Zinc Ion Batteries

Manganese-based material is a prospective cathode material for aqueous zinc ion batteries(ZIBs)by virtue of its high theoretical capacity,high operating voltage,and low price.However,the manganese dissolution during the electrochemical reaction causes its electrochemical cycling stability to be undesirable.In this work,heterointerface engineering-induced oxygen defects are introduced into heterostructure MnO_(2)(δa-MnO_(2))by in situ electrochemical activation to inhibit manganese dissolution for aqueous zinc ion batteries.Meanwhile,the heterointerface between the disordered amorphous and the crystalline MnO_(2)ofδa-MnO_(2)is decisive for the formation of oxygen defects.And the experimental results indicate that the manganese dissolution ofδa-MnO_(2)is considerably inhibited during the charge/discharge cycle.Theoretical analysis indicates that the oxygen defect regulates the electronic and band structure and the Mn-O bonding state of the electrode material,thereby promoting electron transport kinetics as well as inhibiting Mn dissolution.Consequently,the capacity ofδa-MnO_(2)does not degrade after 100 cycles at a current density of 0.5 Ag^(-1)and also 91%capacity retention after 500cycles at 1 Ag^(-1).This study provides a promising insight into the development of high-performance manganese-based cathode materials through a facile and low-cost strategy.

Impact Analysis of Microscopic Defect Types on the Macroscopic Crack Propagation in Sintered Silver Nanoparticles

Sintered silver nanoparticles(AgNPs)arewidely used in high-power electronics due to their exceptional properties.However,the material reliability is significantly affected by various microscopic defects.In this work,the three primary micro-defect types at potential stress concentrations in sintered AgNPs are identified,categorized,and quantified.Molecular dynamics(MD)simulations are employed to observe the failure evolution of different microscopic defects.The dominant mechanisms responsible for this evolution are dislocation nucleation and dislocation motion.At the same time,this paper clarifies the quantitative relationship between the tensile strain amount and the failure mechanism transitions of the three defect types by defining key strain points.The impact of defect types on the failure process is also discussed.Furthermore,traction-separation curves extracted from microscopic defect evolutions serve as a bridge to connect the macro-scale model.The validity of the crack propagation model is confirmed through tensile tests.Finally,we thoroughly analyze how micro-defect types influence macro-crack propagation and attempt to find supporting evidence from the MD model.Our findings provide a multi-perspective reference for the reliability analysis of sintered AgNPs.

Trace Cobalt Doping and Defect Engineering of High Surface Area α-Ni(OH)_(2) for Electrocatalytic Urea Oxidation

Owing to the intrinsically sluggish kinetics of urea oxidation reaction(UOR)involving a six-electron transfer process,developing efficient UOR electrocatalyst is a great challenge remained to be overwhelmed.Herein,by taking advantage of 2-Methylimidazole,of which is a kind of alkali in water and owns strong coordination ability to Co^(2+)in methanol,trace Co(1.0 mol%)addition was found to induce defect engineering onα-Ni(OH)_(2)in a dual-solvent system of water and methanol.Physical characterization results revealed that the synthesized electrocatalyst(WM-Ni_(0.99)Co_(0.01)(OH)_(2))was a kind of defective nanosheet with thickness around 5-6 nm,attributing to the synergistic effect of Co doping and defect engineering,its electron structure was finely altered,and its specific surface a rea was tremendously enlarged from 68 to 172.3 m^(2)g^(-1).With all these merits,its overpotential to drive 10 mA cm^(-2)was reduced by 110 mV.Besides,the interfacial behavior of UOR was also well deciphered by operando electrochemical impedance spectroscopy.

YOLO-DD:Improved YOLOv5 for Defect Detection

As computer technology continues to advance,factories have increasingly higher demands for detecting defects.However,detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes.To address this issue,this paper proposes YOLO-DD,a defect detectionmodel based on YOLOv5 that is effective and robust.To improve the feature extraction process and better capture global information,the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer(RDAT).Additionally,an Information Gap Filling Strategy(IGFS)is proposed to improve the fusion of features at different scales.The classic lightweight attention mechanism Squeeze-and-Excitation(SE)module is also incorporated into the neck section to enhance feature expression and improve the model’s performance.Experimental results on the NEU-DET dataset demonstrate that YOLO-DDachieves competitive results compared to state-of-the-art methods,with a 2.0% increase in accuracy compared to the original YOLOv5,achieving 82.41% accuracy and38.25FPS(framesper second).Themodel is also testedon a self-constructed fabric defect dataset,and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5,demonstrating its stability and generalization ability.The high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection.

Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

Software Defect Prediction Method Based on Stable Learning

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.

Attention-relation network for mobile phone screen defect classification via a few samples

How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens.An attention-relation network for the mobile phone screen defect classification is proposed in this paper.The architecture of the attention-relation network contains two modules:a feature extract module and a feature metric module.Different from other few-shot models,an attention mechanism is applied to metric learning in our model to measure the distance between features,so as to pay attention to the correlation between features and suppress unwanted information.Besides,we combine dilated convolution and skip connection to extract more feature information for follow-up processing.We validate attention-relation network on the mobile phone screen defect dataset.The experimental results show that the classification accuracy of the attentionrelation network is 0.9486 under the 5-way 1-shot training strategy and 0.9039 under the 5-way 5-shot setting.It achieves the excellent effect of classification for mobile phone screen defects and outperforms with dominant advantages.

Strip steel surface defect detection algorithm based on improved Faster R-CNN

To solve the problems of the low accuracy and poor real-time performance of traditional strip steel surface defect detection meth-ods,which are caused by the characteristics of many kinds,complex shapes,and different scales of strip surface defects,a strip steel surface defect detection algorithm based on improved Faster R-CNN is proposed.Firstly,the residual convolution module is inserted into the Swin Transformer network module to form the RC-Swin Transformer network module,and the RC-Swin Transformer module is introduced into the backbone network of the traditional Faster R-CNN to enhance the ability of the network to extract the global feature information of the image and adapt to the complex shape of the strip steel surface defect.To improve the attention of the network to defects in the image,a CBAM-BiFPN network module is designed,and then the backbone network is combined with the CBAM-BiFPN network to realize the de-tection and fusion of multi-scale features.The RoI align layer is used instead of the RoI pooling layer to improve the accuracy of defect loca-tion.Finally,Soft NMS is used to achieve non-maximum suppression and remove redundant boxes.In the comparative experiment on the NEU-DET dataset,the improved algorithm improves the mean average precision by 4.2%compared with the Faster R-CNN algorithm,and also improves the average precision by 6.1%and 6.7%for crazing defect and rolled-in scale defect,which are difficult to detect with the Faster R-CNN algorithm.The experiments show that the improvements proposed in the paper effectively improve the detection accuracy of the algorithm and have certain practical value.

High quality repair of osteochondral defects in rats using the extracellular matrix of antler stem cells

BACKGROUND Cartilage defects are some of the most common causes of arthritis.Cartilage lesions caused by inflammation,trauma or degenerative disease normally result in osteochondral defects.Previous studies have shown that decellularized extracellular matrix(ECM)derived from autologous,allogenic,or xenogeneic mesenchymal stromal cells(MSCs)can effectively restore osteochondral integrity.AIM To determine whether the decellularized ECM of antler reserve mesenchymal cells(RMCs),a xenogeneic material from antler stem cells,is superior to the currently available treatments for osteochondral defects.METHODS We isolated the RMCs from a 60-d-old sika deer antler and cultured them in vitro to 70%confluence;50 mg/mL L-ascorbic acid was then added to the medium to stimulate ECM deposition.Decellularized sheets of adipocyte-derived MSCs(aMSCs)and antlerogenic periosteal cells(another type of antler stem cells)were used as the controls.Three weeks after ascorbic acid stimulation,the ECM sheets were harvested and applied to the osteochondral defects in rat knee joints.RESULTS The defects were successfully repaired by applying the ECM-sheets.The highest quality of repair was achieved in the RMC-ECM group both in vitro(including cell attachment and proliferation),and in vivo(including the simultaneous regeneration of well-vascularized subchondral bone and avascular articular hyaline cartilage integrated with surrounding native tissues).Notably,the antler-stem-cell-derived ECM(xenogeneic)performed better than the aMSC-ECM(allogenic),while the ECM of the active antler stem cells was superior to that of the quiescent antler stem cells.CONCLUSION Decellularized xenogeneic ECM derived from the antler stem cell,particularly the active form(RMC-ECM),can achieve high quality repair/reconstruction of osteochondral defects,suggesting that selection of decellularized ECM for such repair should be focused more on bioactivity rather than kinship.

Impact of Atrial Septal Defect Closure on Mortality in Older Patients

Background:Atrial septal defect(ASD)is a common form of adult congenital heart disease that can lead to long-term adverse outcomes if left untreated.Early closure of ASD has been associated with excellent outcomes and lower complication rates.However,there is limited evidence regarding the prognosis of ASD closure in older adults.This study aims to evaluate the mortality rates in older ASD patients with and without closure.Methods:A retrospective cohort study was conducted on patients aged 40 years or older with ASD between 2001 and 2017.Patients were followed up to assess all-cause mortality.Univariable and multivariable analyses were performed to identify the predictors of mortality.A p-value of<0.05 was considered statistically significant.Results:The cohort consisted of 450 patients(mean age 56.6±10.4 years,77.3%female),with 66%aged between 40 and 60 years,and 34%over 60 years.Within the cohort,299 underwent ASD closure(201 with transcatheter and 98 with surgical closure).During the median follow-up duration of 7.9 years,51 patients died.The unadjusted cumulative 10-year rate of mortality was 3%in patients with ASD closure,and 28%in patients without ASD closure(log-rank p<0.001).Multivariable analysis revealed that age(hazard ratio[HR]1.04,95%confidence interval[CI]1.006–1.06,p=0.01),NYHA class(HR 2.75,95%CI 1.63–4.62,p<0.001),blood urea nitrogen(BUN)(HR 1.07,95%CI 1.03–1.12,p<0.001),right ventricular systolic pressure(RVSP)(HR 1.07,95%CI 1.003–1.04,p=0.01),and lack of ASD closure(HR 15.12,95%CI 5.63–40.59,p<0.001)were independently associated with mortality.Conclusion:ASD closure demonstrated favorable outcomes in older patients.Age,NYHA class,BUN,RVSP,and lack of ASD closure were identified as independent factors linked to mortality in this population.

Defect mediated losses and degradation of perovskite solar cells:Origin impacts and reliable characterization techniques

The rapid advancement of halide-based hybrid perovskite materials has garnered significant research attention,particularly in the domain of photovoltaic technology.Owing to their exceptional optoelec-tronic properties,they demonstrated power conversion efficiency(PcE)of over 25%in single junction solar cells.Despite the notable progress in PCE over the past decade,the inherent high defect density pre-senting in perovskite materials gives rise to several loss mechanisms and associated ion migration in per-ovskite solar cells(PsCs)during operational conditions.These factors collectively contribute to a significant stability challenge in PsCs,placing their longevity far behind for commercialization.While numerous reports have explored defects,ion migration,and their impacts on device performance,a com-prehensive correlation between the types of defects and the degradation kinetics of perovskite materials and PsCs has been lacking.In this context,this review aims to provide a comprehensive overview of the origins of defects and ion migration,emphasizing their correlation with the degradation kinetics of per-ovskite materials and PsCs,leveraging reliable characterization techniques.Furthermore,these charac-terization techniques are intended to comprehend loss mechanisms by different passivation approaches to enhance the durability and PCE of PSCs.

Built defects of homogeneous junction to enhance the lithium storage capacity of niobium pentoxide materials

Niobium pentoxide(Nb_(2)O_(5))is deemed one of the promising anode materials for lithium-ion batteries(LIBs)for its outstanding intrinsic fast Li-(de)intercalation kinetics.The specific capacity,however,is still limited,because the(de)intercalation of excessive Li-ions brings the undesired stress to damage Nb_(2)O_(5) crystals.To increase the capacity of Nb_(2)O_(5) and alleviate the lattice distortion caused by stress,numerous homogeneous H-and M-phases junction interfaces were proposed to produce coercive stress within theNb_(2)O_(5)crystals.Such interfaces bring about rich oxygen vacancies with structural shrinkage tendency,which pre-generate coercive stress to resist the expansion stress caused by excessive Li-ions intercalation.Therefore,the synthesized Nb_(2)O_(5) achieves the highest lithium storage capacity of 315 mA h g−1 to date,and exhibits high-rate performance(118 mA h g^(-1) at 20 C)as well as excellent cycling stability(138 mA h g^(-1) at 10 C after 600 cycles).

Visualizing extended defects at the atomic level in a Bi_(2)Sr_(2)CaCu_(2)O8_(+σ) superconducting wire

The microstructure significantly influences the superconducting properties.Herein,the defect structures and atomic arrangements in high-temperature Bi_(2)Sr_(2)CaCu_(2)O8_(+σ) superconducting wire are directly characterized via stateof-the-art scanning transmission electron microscopy.Interstitial oxygen atoms are observed in both the charge reservoir layers and grain boundaries in the doped superconductor.Inclusion phases with varied numbers of CuO_(2) layers are found,and twist interfaces with different angles are identified.This study provides insights into the structures of Bi-2212 wire and lays the groundwork for guiding the design of microstructures and optimizing the production methods to enhance superconducting performance.

Isolated Ventricular Septal Defect: Ultrasound, Therapeutic and Evolutionary Aspects of 85 Cases in the Cardiology Department of the Ignace Deen National Hospital in Conakry

Introduction: Ventricular septal defect (VSD) is the most common congenital heart disease of all congenital heart defects. The aim of this study was to investigate the echographic, therapeutic and evolutionary aspects of ventricular septal defects (VSD) in the general cardiology department of the Hôpital National Ignace Deen. Methods: A retrospective data collection was carried out from January 2018 to December 2023 including 85 cases of isolated IVC was performed. The variables studied were epidemiological, clinical, paraclinical, therapeutic and evolutionary. Results: Of the 320 patients seen during the study period for congenital heart disease, 85 (26.556%) were isolated IVCs. Age at diagnosis ranged from 3 months to 16 years, with an average age of 3.59 years. The most represented ethnic group was the Fulani (50.58%). The 8.24% came from consanguineous marriage versus 22.35%. 91.76% of children had a history of bronchitis. The most common clinical signs found were systolic murmur (90.58%), growth retardation (51.76%). Only 4 cases (4.70%) had a malformation associated with IVC represented by DiGeorges disease (2.35%) and trisomy 21 (2.35%). Nearly half the patients had type IIb VIC (44.71%). The other half were represented by type 1 (18.82%), type IIa (20%), type III (10.59%) and type IV (5.88%). According to site more than two-thirds of VICs (71.64%) were perimembranous in location, followed by infundibular (16.47%) and muscular (11.76%) VICs. In our study 55.29% presented an indication for both surgical intervention and medical treatment, while 16.47% required only medical treatment. In contrast, 28.23% were placed under exclusive surveillance. Of the 47 patients for whom surgery was indicated, 29 (61.17%) underwent surgical repair, while 18 (38.83%) were awaiting confirmation for surgery. Conclusion: VIC is the most common congenital heart disease. An early detection strategy and the establishment of specialized centers could improve the outcome of these children.

Revealing the Role of Defect in 3D Graphene-Based Photocatalytic Composite for Efficient Elimination of Antibiotic and Heavy Metal Combined Pollution

Defect engineering can give birth to novel properties for adsorption and photocatalysis in the control of antibiotics and heavy metal combined pollution with photocatalytic composites.However,the role of defects and the process mechanism are complicated and indefinable.Herein,TiO_(2)/CN/3DC was fabricated and defects were introduced into the tripartite structure with separate O_(2)plasma treatment for the single component.We find that defect engineering can improve the photocatalytic activity,attributing to the increase of the contribution from h^(+)and OH.In contrast to TiO_(2)/CN/3DC with a photocatalytic tetracycline removal rate of 75.2%,the removal rate of TC with D-TiO_(2)/CN/3DC has increased to 88.5%.Moreover,the reactive sites of tetracycline can be increased by adsorbing on the defective composites.The defect construction on TiO_(2)shows the advantages in tetracycline degradation and Cu^(2+)adsorption,but also suffers significant inhibition for the tetracycline degradation in a tetracycline/Cu^(2+)combined system.In contrast,the defect construction on graphene can achieve the cooperative removal of tetracycline and Cu^(2+).These findings can provide new insights into water treatment strategies with defect engineering.

Effects of Initial Defects on Effective Elastic Modulus of Concrete with Mesostructure

An exquisite mesostructure model was presented to predict the effective elastic modulus of concrete,in which concrete is realized as a four-phase composite material consisting of coarse aggregates,mortar matrix,interfacial transition zone(ITZ),and initial defects.With the three-dimensional(3D)finite element(FE)simulation,the highly heterogeneous composite elastic behavior of concrete was modeled,and the predicted results were compared with theoretical estimations for validation.Monte Carlo(MC)simulations were performed with the proposed mesostructure model to investigate the various factors of initial defects influencing the elastic modulus of concrete,such as the shape and concentration(pore volume fraction or crack density)of microspores and microcracks.It is found that the effective elastic modulus of concrete decreases with the increase of initial defects concentration,while the distribution and shape characteristics also exert certain influences due to the stress concentration caused by irregular inclusion shape.

Effect of Vacancy Defects on the Properties of CoS_(2) and FeS_(2)

In order to explore the effect of vacancy defects on the structural,electronic,magnetic and optical properties of CoS_(2) and FeS_(2),first-principles calculation method was used to investigate the alloys.The calculated results of materials without vacancy are consistent with those reported in the literatures,while the results of materials with vacancy defect were different from those of literatures due to the difference vacancy concentration.The Co vacancy defect hardly changes the half-metallic characteristic of CoS_(2).The Fe vacancy defect changes FeS_(2) from semiconductor to half-metal,and the bottom of the spin-down conduction band changes from the p orbital state of S to the d(t_(2g))orbital state of Fe,while the top of the valence band remains the d orbital d(eg)state of Fe.The half-metallic Co vacancy defects of CoS_(2) and Fe vacancy defects of FeS_(2) are expected to be used in spintronic devices.S vacancy defects make both CoS_(2) and FeS_(2) metallic.Both the Co and S vacancy defects lead to the decrease of the magnetic moment of CoS_(2),while both the Fe and S vacancy defects lead to the obvious magnetic property of FeS_(2).Vacancy defects enhance the absorption coefficient of infrared band and long band of visible light obviously,and produce obvious red shift phenomenon,which is expected to be used in photoelectric devices.

A Hybrid Deep Learning and Machine Learning-Based Approach to Classify Defects in Hot Rolled Steel Strips for Smart Manufacturing

Smart manufacturing is a process that optimizes factory performance and production quality by utilizing various technologies including the Internet of Things(IoT)and artificial intelligence(AI).Quality control is an important part of today’s smart manufacturing process,effectively reducing costs and enhancing operational efficiency.As technology in the industry becomes more advanced,identifying and classifying defects has become an essential element in ensuring the quality of products during the manufacturing process.In this study,we introduce a CNN model for classifying defects on hot-rolled steel strip surfaces using hybrid deep learning techniques,incorporating a global average pooling(GAP)layer and a machine learning-based SVM classifier,with the aim of enhancing accuracy.Initially,features are extracted by the VGG19 convolutional block.Then,after processing through the GAP layer,the extracted features are fed to the SVM classifier for classification.For this purpose,we collected images from publicly available datasets,including the Xsteel surface defect dataset(XSDD)and the NEU surface defect(NEU-CLS)datasets,and we employed offline data augmentation techniques to balance and increase the size of the datasets.The outcome of experiments shows that the proposed methodology achieves the highest metrics score,with 99.79%accuracy,99.80%precision,99.79%recall,and a 99.79%F1-score for the NEU-CLS dataset.Similarly,it achieves 99.64%accuracy,99.65%precision,99.63%recall,and a 99.64%F1-score for the XSDD dataset.A comparison of the proposed methodology to the most recent study showed that it achieved superior results as compared to the other studies.