基于CodeBERT和Stacking集成学习的补丁正确性验证方法

近年来,自动程序修复已成为软件工程领域的重要研究课题。然而,现有的自动修复技术大多是基于补丁生成和测试的,在补丁验证环节时间成本很高。此外,由于测试套件的不完备,许多候选补丁虽然能通过测试,但实际上并不正确,从而导致补丁过拟合。为提高补丁验证的效率并缓解补丁过拟合的问题,提出了一种静态的补丁验证方法。该方法首先使用大型预训练模型CodeBERT自动提取缺陷代码片段和补丁代码片段的语义特征,然后使用历史缺陷修复补丁数据训练Stacking集成学习模型,训练之后的模型可以对新的缺陷修复补丁进行有效验证。在Defects4J缺陷数据集相关的1 000个补丁数据上对所提方法的验证能力进行评估。实验结果表明,该方法可以有效地验证补丁的正确性,从而提高补丁验证的效率。

Optimizing electronic structure through point defect engineering for enhanced electrocatalytic energy conversion

Point defect engineering endows catalysts with novel physical and chemical properties,elevating their electrocatalytic efficiency.The introduction of defects emerges as a promising strategy,effectively modifying the electronic structure of active sites.This optimization influences the adsorption energy of intermediates,thereby mitigating reaction energy barriers,altering paths,enhancing selectivity,and ultimately improving the catalytic efficiency of electrocatalysts.To elucidate the impact of defects on the electrocatalytic process,we comprehensively outline the roles of various point defects,their synthetic methodologies,and characterization techniques.Importantly,we consolidate insights into the relationship between point defects and catalytic activity for hydrogen/oxygen evolution and CO_(2)/O_(2)/N_(2) reduction reactions by integrating mechanisms from diverse reactions.This underscores the pivotal role of point defects in enhancing catalytic performance.At last,the principal challenges and prospects associated with point defects in current electrocatalysts are proposed,emphasizing their role in advancing the efficiency of electrochemical energy storage and conversion materials.

Defect Engineering with Rational Dopants Modulation for High‑Temperature Energy Harvesting in Lead‑Free Piezoceramics

High temperature piezoelectric energy harvester(HTPEH)is an important solution to replace chemical battery to achieve independent power supply of HT wireless sensors.However,simultaneously excellent performances,including high figure of merit(FOM),insulation resistivity(ρ)and depolarization temperature(Td)are indispensable but hard to achieve in lead-free piezoceramics,especially operating at 250°C has not been reported before.Herein,well-balanced performances are achieved in BiFeO3–BaTiO3 ceramics via innovative defect engineering with respect to delicate manganese doping.Due to the synergistic effect of enhancing electrostrictive coefficient by polarization configuration optimization,regulating iron ion oxidation state by high valence manganese ion and stabilizing domain orientation by defect dipole,comprehensive excellent electrical performances(Td=340°C,ρ250°C>10^(7)Ωcm and FOM_(250°C)=4905×10^(–15)m^(2)N^(−1))are realized at the solid solubility limit of manganese ions.The HT-PEHs assembled using the rationally designed piezoceramic can allow for fast charging of commercial electrolytic capacitor at 250°C with high energy conversion efficiency(η=11.43%).These characteristics demonstrate that defect engineering tailored BF-BT can satisfy high-end HT-PEHs requirements,paving a new way in developing selfpowered wireless sensors working in HT environments.

Steel Surface Defect Recognition in Smart Manufacturing Using Deep Ensemble Transfer Learning-Based Techniques

Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence(AI)and internet of things(IoT)to enhance efficiency,reduce costs,and ensure product quality.In light of the recent advancement of Industry 4.0,identifying defects has become important for ensuring the quality of products during the manufacturing process.In this research,we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network(CNN)architectures:VGG16,VGG19,Xception,and Mobile-Net V2,compensating for their individual weaknesses.We evaluated our methodology on the Xsteel surface defect dataset(XSDD),which comprises seven different classes.The ensemble methodology integrated the predictions of individual models through two methods:model averaging and weighted averaging.Our evaluation showed that the model averaging ensemble achieved an accuracy of 98.89%,a recall of 98.92%,a precision of 99.05%,and an F1-score of 98.97%,while the weighted averaging ensemble reached an accuracy of 99.72%,a recall of 99.74%,a precision of 99.67%,and an F1-score of 99.70%.The proposed weighted averaging ensemble model outperformed the model averaging method and the individual models in detecting defects in terms of accuracy,recall,precision,and F1-score.Comparative analysis with recent studies also showed the superior performance of our methodology.

Defect Engineering:Can it Mitigate Strong Coulomb Effect of Mg^(2+)in Cathode Materials for Rechargeable Magnesium Batteries?

Rechargeable magnesium batteries(RMBs)have been considered a promising“post lithium-ion battery”system to meet the rapidly increasing demand of the emerging electric vehicle and grid energy storage market.However,the sluggish diffusion kinetics of bivalent Mg^(2+)in the host material,related to the strong Coulomb effect between Mg^(2+)and host anion lattices,hinders their further development toward practical applications.Defect engineering,regarded as an effective strategy to break through the slow migration puzzle,has been validated in various cathode materials for RMBs.In this review,we first thoroughly understand the intrinsic mechanism of Mg^(2+)diffusion in cathode materials,from which the key factors affecting ion diffusion are further presented.Then,the positive effects of purposely introduced defects,including vacancy and doping,and the corresponding strategies for introducing various defects are discussed.The applications of defect engineering in cathode materials for RMBs with advanced electrochemical properties are also summarized.Finally,the existing challenges and future perspectives of defect engineering in cathode materials for the overall high-performance RMBs are described.

Controversies in fecal incontinence

Fecal incontinence is a common condition that can significantly impact patients’quality of life.Obstetric anal sphincter injury and anorectal surgeries are common etiologies.Endoanal ultrasound and anorectal manometry are important diagnostic tools for evaluating patients.There are various treatment options,including diet,lifestyle modifications,drugs,biofeedback therapy,tibial and sacral nerve neuromodulation therapy,and surgery.In this editorial,we will discuss current controversies and novel approaches to fecal incontinence.Screening for asymptomatic anal sphincter defects after obstetric anal sphincter injury and in patients with inflammatory bowel disease is not generally recommended,but may be helpful in selected patients.The Garg incontinence score is a new score that includes the assessment of solid,liquid,flatus,mucous,stress and urge fecal incontinence.Novel tests such as translumbosacral anorectal magnetic stimulation and novel therapies such as translumbosacral neuromodulation therapy are promising diagnostic and treatment options,for both fecal incontinence and neuropathy.Home biofeedback therapy can overcome some limitations of the office-based therapy.Skeletal muscle-derived cell implantation of the external anal sphincter has been further studied as a possible treatment option.Sacral neuromodulation may be useful in scleroderma,congenital fecal incontinence and inflammatory bowel disease but merits further study.

Beta thalassemia syndromes:New insights

Beta thalassemia(β-thalassemia)syndromes are a heterogeneous group of inherited hemoglobinopathies caused by molecular defects in the beta-globin gene that lead to the impaired synthesis of beta-globin chains of the hemoglobin.The hallmarks of the disease include ineffective erythropoiesis,chronic hemolytic anemia,and iron overload.Clinical presentation ranges from asymptomatic carriers to severe anemia requiring lifelong blood transfusions with subsequent devastating complications.The management of patients with severeβ-thalassemia represents a global health problem,particularly in low-income countries.Until recently,management strategies were limited to regular transfusions and iron chelation therapy,with allogeneic hematopoietic stem cell transplantation available only for a subset of patients.Better understanding of the underlying pathophysiological mechanisms ofβ-thalassemia syndromes and associated clinical phenotypes has paved the way for novel therapeutic options,including pharmacologic enhancers of effective erythropoiesis and gene therapy.

Masquelet technique using an allogeneic cortical bone graft for a large bone defect:A case report

BACKGROUND The induced-membrane technique was initially described by Masquelet as an effective treatment for large bone defects,especially those caused by infection.Here,we report a case of chronic osteomyelitis of the radius associated with a 9 cm bone defect,which was filled with a large allogeneic cortical bone graft from a bone bank.Complete bony union was achieved after 14 months of follow-up.Previous studies have used autogenous bone as the primary bone source for the Masquelet technique;in our case,the exclusive use of allografts is as successful as the use of autologous bone grafts.With the advent of bone banks,it is possible to obtain an unlimited amount of allograft,and the Masquelet technique may be further improved based on this new way of bone grafting.CASE SUMMARY In this study,we reported a case of repair of a long bone defect in a 40-year-old male patient,which was characterized by the utilization of allograft cortical bone combined with the Masquelet technique for the treatment of the patient's long bone defect in the forearm.The patient's results of functional recovery of the forearm were surprising,which further deepens the scope of application of Masquelet technique and helps to strengthen the efficacy of Masquelet technique in the treatment of long bones indeed.CONCLUSION Allograft cortical bone combined with the Masquelet technique provides a new method of treatment to large bone defect.

Visual electrophysiological assessment of children with poor response to treatment for functional amblyopia

AIM:To assess the visual electrophysiological outcomes in children with functional amblyopia who exhibited poor response to conventional treatment.METHODS:Twenty-one children with functional amblyopia,aged 5.7±2.1y(range:4-10y),underwent comprehensive ophthalmic and refractive evaluations.Spectral domain-optical coherence tomography(SD-OCT)and multifocal electroretinography(mfERG)were conducted to analyze the macular retinal thickness and the first-order response P1 ring of the mfERG in the amblyopic eye(AE)compared to the fellow good eye(GE).RESULTS:Initially,visual acuity in the AE ranged from 20/800 to 20/40,while the GE exhibited a range of 20/25 to 20/20(P<0.01).After 6mo of treatment,17 patients demonstrated improved visual acuity in the AE to 20/50 or better,while 4 children showed no improvement.SD-OCT revealed comparable macular and optic disc structures between the AE and GE.Prior to treatment,the mfERG P1 ring amplitude was significantly reduced in the AE compared to GE(P<0.05).The AE/GE ratio of P1 ring amplitude showed significant improvement post-treatment.However,a smaller AE/GE ratio before treatment was associated with poorer improvement post-treatment.CONCLUSION:In the management of functional amblyopia,a thorough assessment of amblyopic eye examinations is crucial.Approximately 20%of amblyopic eyes may not achieve significant improvement in visual acuity,despite the absence of detectable organic retinal abnormalities.mfERG may reveal underlying abnormalities.Integrating mfERG into initial assessments or treatment follow-ups can aid in identifying potential hidden retinal defects and predicting the prognosis of the amblyopic eye.

Lamb wave TDTE super-resolution imaging assisted by deep learning

Ultrasonic Lamb waves undergo complex mode conversion and diffraction at non-penetrating defects, such as plate corrosion and cracks. Lamb wave imaging has a resolution limit due to the guided wave dispersion characteristics and Rayleigh criterion limitations. In this paper, a full convolutional network is designed to segment and reconstruct the received signals, enabling the automatic identification of target modalities. This approach eliminates clutter and mode conversion interference when calculating direct and accompanying acoustic fields in time-domain topological energy(TDTE) imaging.Subsequently, the measured accompanying acoustic field is reversed for adaptive focusing on defects and enhance the imaging quality. To circumvent the limitations of the Rayleigh criterion, the direct acoustic field and the accompanying acoustic field were fused to characterize the pixel distribution in the imaging region, achieving Lamb wave super-resolution imaging. Experimental results indicate that compared to the sign coherence factor-total focusing method(SCF-TFM),the proposed method achieves a 31.41% improvement in lateral resolution and a 29.53% increase in signal-to-noise ratio for single-blind-hole defects. In the case of multiple-blind-hole defects with spacings greater than the Rayleigh criterion resolution limit, it exhibits a 27.23% enhancement in signal-to-noise ratio. On the contrary, when the defect spacings are relatively smaller than the limit, this method has a higher resolution limit than SCF-TFM in super-resolution imaging.

Biomechanical Study of Different Scaffold Designs for Reconstructing a Traumatic Distal Femur Defect Using Patient-Specific Computational Modeling

Reconstruction of a traumatic distal femur defect remains a therapeutic challenge.Bone defect implants have been proposed to substitute the bone defect,and their biomechanical performances can be analyzed via a numerical approach.However,the material assumptions for past computational human femur simulations were mainly homogeneous.Thus,this study aimed to design and analyze scaffolds for reconstructing the distal femur defect using a patient-specific finite element modeling technique.A three-dimensional finite element model of the human femur with accurate geometry and material distribution was developed using the finite element method and material mapping technique.An intact femur and a distal femur defect model treated with nine microstructure scaffolds and two solid scaffolds were investigated and compared under a single-leg stance loading.The results showed that the metal solid scaffold design could provide the most stable fixation for reconstructing the distal femur defect.However,the fixation stability was affected by various microstructure designs and pillar diameters.A microstructure scaffold can be designed to satisfy all the biomechanical indexes,opening up future possibilities for more stable reconstructions.A three-dimensional finite element model of the femur with real bone geometry and bone material distribution can be developed,and this patient-specific femur model can be used for studying other femoral fractures or injuries,paving the way for more comprehensive research in the field.Besides,this patient-specific finite element modeling technique can also be applied to developing other human or animal bone models,expanding the scope of biomechanical research.

From Imperfection to Perfection: Advanced 3D Facial Reconstruction Using MICA Models and Self-Supervision Learning

Research on reconstructing imperfect faces is a challenging task.In this study,we explore a data-driven approach using a pre-trained MICA(MetrIC fAce)model combined with 3D printing to address this challenge.We propose a training strategy that utilizes the pre-trained MICA model and self-supervised learning techniques to improve accuracy and reduce the time needed for 3D facial structure reconstruction.Our results demonstrate high accuracy,evaluated by the geometric loss function and various statistical measures.To showcase the effectiveness of the approach,we used 3D printing to create a model that covers facial wounds.The findings indicate that our method produces a model that fits well and achieves comprehensive 3D facial reconstruction.This technique has the potential to aid doctors in treating patients with facial injuries.

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.

Recent progress in the research on magnesium and magnesium alloy foils:A short review

Magnesium and magnesium alloy foils have great potential for application in battery anodes,electromagnetic shielding,optics and acoustics,and biology because of their excellent specific damping,internal dissipation coefficients,magnetic and electrical conductivities,as well as high theoretical specific capacity.However,magnesium alloys exhibit poor deformation ability due to their hexagonal close-packed crystal structure.Preparing magnesium and magnesium alloy foils with thicknesses of less than 0.1 mm is difficult because of surface oxidation and grain growth at high temperatures or severe anisotropy after cold rolling that leads to cracks.Numerous methods have been applied to prepare magnesium alloy foils.They include warm rolling,cold rolling,accumulative roll bonding,electric plastic rolling,and on-line heating rolling.Defects of magnesium and magnesium alloy foils during preparation,such as edge cracks and breakage,are important factors for consideration.Herein,the current status of the research on magnesium and magnesium alloy foils is summarized from the aspects of foil preparation,defect control,performance characterization,and application prospects.The advantages and disadvantages of different preparation methods and defect(edge cracks and breakage)mechanisms in the preparation of foils are identified.

Icariin accelerates bone regeneration by inducing osteogenesisangiogenesis coupling in rats with type 1 diabetes mellitus

BACKGROUND Icariin(ICA),a natural flavonoid compound monomer,has multiple pharmacological activities.However,its effect on bone defect in the context of type 1 diabetes mellitus(T1DM)has not yet been examined.AIM To explore the role and potential mechanism of ICA on bone defect in the context of T1DM.METHODS The effects of ICA on osteogenesis and angiogenesis were evaluated by alkaline phosphatase staining,alizarin red S staining,quantitative real-time polymerase chain reaction,Western blot,and immunofluorescence.Angiogenesis-related assays were conducted to investigate the relationship between osteogenesis and angiogenesis.A bone defect model was established in T1DM rats.The model rats were then treated with ICA or placebo and micron-scale computed tomography,histomorphometry,histology,and sequential fluorescent labeling were used to evaluate the effect of ICA on bone formation in the defect area.RESULTS ICA promoted bone marrow mesenchymal stem cell(BMSC)proliferation and osteogenic differentiation.The ICA treated-BMSCs showed higher expression levels of osteogenesis-related markers(alkaline phosphatase and osteocalcin)and angiogenesis-related markers(vascular endothelial growth factor A and platelet endothelial cell adhesion molecule 1)compared to the untreated group.ICA was also found to induce osteogenesis-angiogenesis coupling of BMSCs.In the bone defect model T1DM rats,ICA facilitated bone formation and CD31hiEMCNhi type H-positive capillary formation.Lastly,ICA effectively accelerated the rate of bone formation in the defect area.CONCLUSION ICA was able to accelerate bone regeneration in a T1DM rat model by inducing osteogenesis-angiogenesis coupling of BMSCs.

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.

Advances and challenges in direct additive manufacturing of dense ceramic oxides

Ceramic oxides,renowned for their exceptional combination of mechanical,thermal,and chemical properties,are indispensable in numerous crucial applications across diverse engineering fields.However,conventional manufacturing methods frequently grapple with limitations,such as challenges in shaping intricate geometries,extended processing durations,elevated porosity,and substantial shrinkage deformations.Direct additive manufacturing(dAM)technology stands out as a state-of-the-art solution for ceramic oxides production.It facilitates the one-step fabrication of high-performance,intricately designed components characterized by dense structures.Importantly,dAM eliminates the necessity for post-heat treatments,streamlining the manufacturing process and enhancing overall efficiency.This study undertakes a comprehensive review of recent developments in dAM for ceramic oxides,with a specific emphasis on the laser powder bed fusion and laser directed energy deposition techniques.A thorough investigation is conducted into the shaping quality,microstructure,and properties of diverse ceramic oxides produced through dAM.Critical examination is given to key aspects including feedstock preparation,laser-material coupling,formation and control of defects,in-situ monitoring and simulation.This paper concludes by outlining future trends and potential breakthrough directions,taking into account current gaps in this rapidly evolving field.

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.