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 e...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.展开更多
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,inclu...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.展开更多
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,re...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.展开更多
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,th...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.展开更多
Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules.In the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it cha...Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules.In the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challenging to collect defective samples.Additionally,the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions.This paper proposes a novel Lightweight Multiscale Feature Fusion network(LMFF)to address these challenges.The network comprises a feature extraction network,a multi-scale feature fusion module(MFF),and a segmentation network.Specifically,a feature extraction network is proposed to obtain multi-scale feature outputs,and a multi-scale feature fusion module(MFF)is used to fuse multi-scale feature information effectively.In order to capture finer-grained multi-scale information from the fusion features,we propose a multi-scale attention module(MSA)in the segmentation network to enhance the network’s ability for small target detection.Moreover,depthwise separable convolutions are introduced to construct depthwise separable residual blocks(DSR)to reduce the model’s parameter number.Finally,to validate the proposed method’s defect segmentation and localization performance,we constructed three solar cell defect detection datasets:SolarCells,SolarCells-S,and PVEL-S.SolarCells and SolarCells-S are monocrystalline silicon datasets,and PVEL-S is a polycrystalline silicon dataset.Experimental results show that the IOU of our method on these three datasets can reach 68.5%,51.0%,and 92.7%,respectively,and the F1-Score can reach 81.3%,67.5%,and 96.2%,respectively,which surpasses other commonly usedmethods and verifies the effectiveness of our LMFF network.展开更多
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 th...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.展开更多
This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as o...This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.展开更多
Rail defects can pose significant safety risks in railway operations, raising the need for effective detection methods. Acoustic Emission (AE) technology has shown promise for identifying and monitoring these defects,...Rail defects can pose significant safety risks in railway operations, raising the need for effective detection methods. Acoustic Emission (AE) technology has shown promise for identifying and monitoring these defects, and this study evaluates an advanced on-vehicle AE detection approach using bone-conduct sensors—a solution to improve upon previous AE methods of using on-rail sensor installations, which required extensive, costly on-rail sensor networks with limited effectiveness. In response to these challenges, the study specifically explored bone-conduct sensors mounted directly on the vehicle rather than rails by evaluating AE signals generated by the interaction between rails and the train’s wheels while in motion. In this research, a prototype detection system was developed and tested through initial trials at the Nevada Railroad Museum using a track with pre-damaged welding defects. Further testing was conducted at the Transportation Technology Center Inc. (rebranded as MxV Rail) in Colorado, where the system’s performance was evaluated across various defect types and train speeds. The results indicated that bone-conduct sensors were insufficient for detecting AE signals when mounted on moving vehicles. These findings highlight the limitations of contact-based methods in real-world applications and indicate the need for exploring improved, non-contact approaches.展开更多
Solution-processed Cu(In,Ga)Se_(2)(CIGS) solar cells suffer from serious carrier recombination and power conversion efficiency(PCE) loss because of the poor film properties and easy formation of defects.Herein, we pro...Solution-processed Cu(In,Ga)Se_(2)(CIGS) solar cells suffer from serious carrier recombination and power conversion efficiency(PCE) loss because of the poor film properties and easy formation of defects.Herein, we propose Ag&Se co-selenization strategy to enhance the crystallization and passivate harmful defects of the CIGS films. The formation of Ag-Se phase during the selenization process enables the formation of large grains and suppresses the deep level defects. It is found that Ag doping can enlarge the depletion region width, lower the Urbach energy and prolong the carrier lifetime. As a result, a champion solution-processed CIGS solar cell presents a high efficiency of 16.48% with the highly improved opencircuit voltage(VOC) of 662 m V and fill factor(FF) of 75.8%. This work provides an efficient strategy to prepare high quality solution-processed CIGS films for high-performance CIGS solar cells.展开更多
Co_(3)O_(4)possesses both direct and indirect oxidation effects and is considered as a promising catalyst for the oxidation of 5-hydroxymethylfurfural(HMF).However,the enrichment and activation effects of Co_(3)O_(4)o...Co_(3)O_(4)possesses both direct and indirect oxidation effects and is considered as a promising catalyst for the oxidation of 5-hydroxymethylfurfural(HMF).However,the enrichment and activation effects of Co_(3)O_(4)on OH-and HMF are weak,which limits its further application.Metal defect engineering can regulate the electronic structure,optimize the adsorption of intermediates,and improve the catalytic activity by breaking the symmetry of the material,which is rarely involved in the upgrading of biomass.In this work,we prepare Co_(3)O_(4)with metal defects and load the precious metal platinum at the defect sites(PtVco).The results of in-situ characterizatio ns,electrochemical measurements,and theoretical calculations indicate that the reduction of Co-Co coordination number and the formation of Pt-Co bond induce the decrease of electron filling in the antibonding orbitals of Co element.The resulting upward shift of the d-band center of Co combined with the characteristic adsorption of Pt species synergically enhances the enrichment and activation of organic molecules and OH species,thus exhibiting excellent HMF oxidation activity(including a lower onset potential(1.14 V)and 19 times higher current density than pure Co_(3)O_(4)at 1.35 V).In summary,this work explores the adsorption enhancement mechanism of metal defect sites modified by precious metal in detail,provides a new option for improving the HMF oxidation activity of cobalt-based materials,broadens the application field of metal defect based materials,and gives an innovative guidance for the functional utilization of metal defect sites in biomass conversion.展开更多
Widely used spin-coated nickle oxide (NiOx) based perovskite solar cells often suffer from severe interfacial reactions between the NiOxand adjacent perovskite layers due to surface defect states,which inherently impa...Widely used spin-coated nickle oxide (NiOx) based perovskite solar cells often suffer from severe interfacial reactions between the NiOxand adjacent perovskite layers due to surface defect states,which inherently impair device performance in a long-term view,even with surface molecule passivation.In this study,we developed high-quality magnetron-sputtered NiOxthin films through detailed process optimization,and compared systematically sputtered and spin-coated NiOxthin film surfaces from materials to devices.These sputtered NiOxfilms exhibit improved crystallinity,smoother surfaces,and significantly reduced Ni3+or Ni vacancies compared to their spin-coated counterparts.Consequently,the interface between the perovskite and sputtered NiOxfilm shows a substantially reduced density of defect states.Perovskite solar cells (PSCs) fabricated with our optimally sputtered NiOxfilms achieved a high power conversion efficiency (PCE) of up to 19.93%and demonstrated enhanced stability,maintaining 86.2% efficiency during 500 h of maximum power point tracking under one standard sun illumination.Moreover,with the surface modification using (4-(2,7-dibromo-9,9-dimethylacridin-10(9H)-yl)butyl)p hosphonic acid (DMAcPA),the device PCE was further promoted to 23.07%,which is the highest value reported for sputtered NiOxbased PSCs so far.展开更多
Correction to:Nano-Micro Lett.(2025)17:24 https://doi.org/10.1007/s40820-024-01515-0 Following publication of the original article[1],the authors reported the author list needed to be updated because the last three au...Correction to:Nano-Micro Lett.(2025)17:24 https://doi.org/10.1007/s40820-024-01515-0 Following publication of the original article[1],the authors reported the author list needed to be updated because the last three author names were duplicated.The correct author list has been provided in this Correction.The original article[1]has been corrected.展开更多
Defective phononic crystals(PnCs)have enabled spatial localization and quantitative amplification of elastic wave energy.Most previous research has focused on applications such as narrow-bandpass filters,ultrasonic se...Defective phononic crystals(PnCs)have enabled spatial localization and quantitative amplification of elastic wave energy.Most previous research has focused on applications such as narrow-bandpass filters,ultrasonic sensors,and piezoelectric energy harvesters,typically operating under the assumption of an external elastic wave incidence.Recently,a novel approach that uses defective PnCs as ultrasonic actuators to generate amplified waves has emerged.However,the existing studies are limited to the generation of either longitudinal or bending waves,with no research addressing the concurrent generation of both.Hence,this paper proposes a straightforward methodology for the concurrent generation and amplification of both wave types utilizing defect modes at independent defect-band frequencies.Bimorph piezoelectric elements are attached to the defect,with each element connected to independent external voltage sources.By precisely adjusting the magnitude and temporal phase differences between the voltage sources,concurrently amplified wave generation is achieved.The paper highlights the advantages of the proposed analytical model.This model is both computationally time-efficient and accurate,in comparison with the COMSOL simulation results.For instance,in case studies,the analytical model reduces the computational time from one hour to mere seconds,while maintaining acceptable error rates of 1%in peak frequencies.This concurrent wave-generation methodology opens new avenues for applications in rotating machinery fault diagnosis,structural health monitoring,and medical imaging.展开更多
Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation.However,existing detection methods often struggle with challenges such as com...Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation.However,existing detection methods often struggle with challenges such as complex defect morphology,texture similarity,and fuzzy edges,leading to poor accuracy and missed detections.In order to resolve these problems,we propose MSCM-Net(Multi-Scale Cross-Modal Network),a multiscale cross-modal framework focused on detecting rail surface defects.MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps,effectively capturing and enhancing features at different scales for each modality.To further enrich feature representation and improve edge detection in blurred areas,we propose a multi-scale void fusion module that integrates multi-scale feature information.To improve cross-modal feature fusion,we develop a cross-enhanced fusion module that transfers fused features between layers to incorporate interlayer information.We also introduce a multimodal feature integration module,which merges modality-specific features from separate decoders into a shared decoder,enhancing detection by leveraging richer complementary information.Finally,we validate MSCM-Net on the NEU RSDDS-AUG RGB-depth dataset,comparing it against 12 leading methods,and the results show that MSCM-Net achieves superior performance on all metrics.展开更多
The presence of SnZn-related defects in Cu_(2)ZnSn(S,Se)_(4)(CZTSSe)absorber results in large irreversible energy loss and extra irreversible electron-hole non-radiative recombination,thus hindering the efficiency enh...The presence of SnZn-related defects in Cu_(2)ZnSn(S,Se)_(4)(CZTSSe)absorber results in large irreversible energy loss and extra irreversible electron-hole non-radiative recombination,thus hindering the efficiency enhancement of CZTSSe devices.Although the incorporation of Ag in CZTSSe can effectively suppress the SnZn-related defects and significantly improve the resulting cell performance,an excellent efficiency has not been achieved to date primarily owing to the poor electrical-conductivity and the low carrier density of the CZTSSe film induced by Ag substitution.Herein,this study exquisitely devises an Ag/H co-doping strategy in CZTSSe absorber via Ag substitution programs followed by hydrogen-plasma treatment procedure to suppress SnZn defects for achieving efficient CZTSSe devices.In-depth investigation results demonstrate that the incorporation of H in Ag-based CZTSSe absorber is expected to improve the poor electrical-conductivity and the low carrier density caused by Ag substitution.Importantly,the C=O and O-H functional groups induced by hydrogen incorporation,serving as an electron donor,can interact with under-coordinated cations in CZTSSe material,effectively passivating the SnZn-related defects.Consequently,the incorporation of an appropriate amount of Ag/H in CZTSSe mitigates carrier non-radiative recombination,prolongs minority carrier lifetime,and thus yields a champion efficiency of 14.74%,showing its promising application in kesterite-based CZTSSe devices.展开更多
The primary goal of software defect prediction (SDP) is to pinpoint code modules that are likely to contain defects, thereby enabling software quality assurance teams to strategically allocate their resources and manp...The primary goal of software defect prediction (SDP) is to pinpoint code modules that are likely to contain defects, thereby enabling software quality assurance teams to strategically allocate their resources and manpower. Within-project defect prediction (WPDP) is a widely used method in SDP. Despite various improvements, current methods still face challenges such as coarse-grained prediction and ineffective handling of data drift due to differences in project distribution. To address these issues, we propose a fine-grained SDP method called DIDP (drift-immune defect prediction), based on drift-immune graph neural networks (DI-GNN). DIDP converts source code into graph representations and uses DI-GNN to mitigate data drift at the model level. It also analyses key statements leading to file defects for a more detailed SDP approach. We evaluated the performance of DIDP in WPDP by examining its file-level and statement-level accuracy compared to state-of-the-art methods, and by examining its cross-project prediction accuracy. The results of the experiment show that DIDP showed significant improvements in F1-score and Recall@Top20%LOC compared to existing methods, even with large software version changes. DIDP also performed well in cross-project SDP. Our study demonstrates that DIDP achieves impressive prediction results in WPDP, effectively mitigating data drift and accurately predicting defective files. Additionally, DIDP can rank the risk of statements in defective files, aiding developers and testers in identifying potential code issues.展开更多
Software defect prediction aims to use measurement data of code and historical defects to predict potential problems,optimize testing resources and defect management.However,current methods face challenges:(1)Coarse-g...Software defect prediction aims to use measurement data of code and historical defects to predict potential problems,optimize testing resources and defect management.However,current methods face challenges:(1)Coarse-grained file level detection cannot accurately locate specific defects.(2)Fine-grained line-level defect prediction methods rely solely on local information of a single line of code,failing to deeply analyze the semantic context of the code line and ignoring the heuristic impact of line-level context on the code line,making it difficult to capture the interaction between global and local information.Therefore,this paper proposes a telecontext-enhanced recursive interactive attention fusion method for line-level defect prediction(TRIA-LineDP).Firstly,using a bidirectional hierarchical attention network to extract semantic features and contextual information from the original code lines as the basis.Then,the extracted contextual information is forwarded to the telecontext capture module to aggregate the global context,thereby enhancing the understanding of broader code dynamics.Finally,a recursive interaction model is used to simulate the interaction between code lines and line-level context,passing information layer by layer to enhance local and global information exchange,thereby achieving accurate defect localization.Experimental results from within-project defect prediction(WPDP)and cross-project defect prediction(CPDP)conducted on nine different projects(encompassing a total of 32 versions)demonstrated that,within the same project,the proposed methods will respectively recall at top 20%of lines of code(Recall@Top20%LOC)and effort at top 20%recall(Effort@Top20%Recall)has increased by 11%–52%and 23%–77%.In different projects,improvements of 9%–60%and 18%–77%have been achieved,which are superior to existing advanced methods and have good detection performance.展开更多
Objective Exposure to polycyclic aromatic hydrocarbons(PAHs)or metal(loid)s individually has been associated with neural tube defects(NTDs).However,the impacts of PAH and metal(loid)co-exposure and potential interacti...Objective Exposure to polycyclic aromatic hydrocarbons(PAHs)or metal(loid)s individually has been associated with neural tube defects(NTDs).However,the impacts of PAH and metal(loid)co-exposure and potential interaction effects on NTD risk remain unclear.We conducted a case-control study in China among population with a high prevalence of NTDs to investigate the combined effects of PAH and metal(loid)exposures on the risk of NTD.Methods Cases included 80 women who gave birth to offspring with NTDs,whereas controls were 50 women who delivered infants with no congenital malformations.We analyzed the levels of placental PAHs using gas chromatography and mass spectrometry,PAH-DNA adducts with 32P-post-labeling method,and metal(loid)s with an inductively coupled plasma mass spectrometer.Unconditional logistic regression was employed to estimate the associations between individual exposures and NTDs.Least absolute shrinkage and selection operator(LASSO)penalized regression models were used to select a subset of exposures,while additive interaction models were used to identify interaction effects.Results In the single-exposure models,we found that eight PAHs,PAH-DNA adducts,and 28 metal(loid)s were associated with NTDs.Pyrene,selenium,molybdenum,cadmium,uranium,and rubidium were selected through LASSO regression and were statistically associated with NTDs in the multiple-exposure models.Women with high levels of pyrene and molybdenum or pyrene and selenium exhibited significantly increased risk of having offspring with NTDs,indicating that these combinations may have synergistic effects on the risk of NTDs.Conclusion Our findings suggest that individual PAHs and metal(loid)s,as well as their interactions,may be associated with the risk of NTDs,which warrants further investigation.展开更多
Newly built tunnels often encounter a series of defects within the first few years of operation.If not promptly addressed and reinforced,these defects threaten the tunnel's durability and stability and bring sever...Newly built tunnels often encounter a series of defects within the first few years of operation.If not promptly addressed and reinforced,these defects threaten the tunnel's durability and stability and bring severe challenges to its safe operation.This study aims to explore reinforcement techniques for addressing defects in newly built tunnels.The research begins with an analysis of common defects found in newly built tunnels,followed by a case study of the Jinfeng Tunnel in Chongqing,examining the post-construction defects.The actual reinforcement strategies and methods employed for the tunnel are then discussed.Finally,based on the research findings,this study provides insights and references for tunnel operation and construction units in China,aiming to enhance the overall quality of tunnel engineering in the country,align with sustainable development goals,and promote further improvements at a macro level.展开更多
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.展开更多
基金supported by the National Natural Science Foundation of China(U21A20281)the Special Fund for Young Teachers from Zhengzhou University(JC23557030,JC23257011)+1 种基金the Key Research Projects of Higher Education Institutions of Henan Province(24A530009)the Project of Zhongyuan Critical Metals Laboratory(GJJSGFYQ202336).
文摘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.
基金supported by the National Natural Science Foundation of China(Grant Nos.52272103 and 52072010)Beijing Natural Science Foundation(Grant Nos.2242029 and JL23004).
文摘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.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2022R1I1A3063493).
文摘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.
基金support of the National Natural Science Foundation of China(Grant No.22225801,22178217 and 22308216)supported by the Fundamental Research Funds for the Central Universities,conducted at Tongji University.
文摘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.
基金supported in part by the National Natural Science Foundation of China under Grants 62463002,62062021 and 62473033in part by the Guiyang Scientific Plan Project[2023]48–11,in part by QKHZYD[2023]010 Guizhou Province Science and Technology Innovation Base Construction Project“Key Laboratory Construction of Intelligent Mountain Agricultural Equipment”.
文摘Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules.In the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challenging to collect defective samples.Additionally,the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions.This paper proposes a novel Lightweight Multiscale Feature Fusion network(LMFF)to address these challenges.The network comprises a feature extraction network,a multi-scale feature fusion module(MFF),and a segmentation network.Specifically,a feature extraction network is proposed to obtain multi-scale feature outputs,and a multi-scale feature fusion module(MFF)is used to fuse multi-scale feature information effectively.In order to capture finer-grained multi-scale information from the fusion features,we propose a multi-scale attention module(MSA)in the segmentation network to enhance the network’s ability for small target detection.Moreover,depthwise separable convolutions are introduced to construct depthwise separable residual blocks(DSR)to reduce the model’s parameter number.Finally,to validate the proposed method’s defect segmentation and localization performance,we constructed three solar cell defect detection datasets:SolarCells,SolarCells-S,and PVEL-S.SolarCells and SolarCells-S are monocrystalline silicon datasets,and PVEL-S is a polycrystalline silicon dataset.Experimental results show that the IOU of our method on these three datasets can reach 68.5%,51.0%,and 92.7%,respectively,and the F1-Score can reach 81.3%,67.5%,and 96.2%,respectively,which surpasses other commonly usedmethods and verifies the effectiveness of our LMFF network.
文摘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.
基金funded by Woosong University Academic Research 2024.
文摘This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.
文摘Rail defects can pose significant safety risks in railway operations, raising the need for effective detection methods. Acoustic Emission (AE) technology has shown promise for identifying and monitoring these defects, and this study evaluates an advanced on-vehicle AE detection approach using bone-conduct sensors—a solution to improve upon previous AE methods of using on-rail sensor installations, which required extensive, costly on-rail sensor networks with limited effectiveness. In response to these challenges, the study specifically explored bone-conduct sensors mounted directly on the vehicle rather than rails by evaluating AE signals generated by the interaction between rails and the train’s wheels while in motion. In this research, a prototype detection system was developed and tested through initial trials at the Nevada Railroad Museum using a track with pre-damaged welding defects. Further testing was conducted at the Transportation Technology Center Inc. (rebranded as MxV Rail) in Colorado, where the system’s performance was evaluated across various defect types and train speeds. The results indicated that bone-conduct sensors were insufficient for detecting AE signals when mounted on moving vehicles. These findings highlight the limitations of contact-based methods in real-world applications and indicate the need for exploring improved, non-contact approaches.
基金National Natural Science Foundation of China (62104061, 62074052, 61974173 and 52072327)。
文摘Solution-processed Cu(In,Ga)Se_(2)(CIGS) solar cells suffer from serious carrier recombination and power conversion efficiency(PCE) loss because of the poor film properties and easy formation of defects.Herein, we propose Ag&Se co-selenization strategy to enhance the crystallization and passivate harmful defects of the CIGS films. The formation of Ag-Se phase during the selenization process enables the formation of large grains and suppresses the deep level defects. It is found that Ag doping can enlarge the depletion region width, lower the Urbach energy and prolong the carrier lifetime. As a result, a champion solution-processed CIGS solar cell presents a high efficiency of 16.48% with the highly improved opencircuit voltage(VOC) of 662 m V and fill factor(FF) of 75.8%. This work provides an efficient strategy to prepare high quality solution-processed CIGS films for high-performance CIGS solar cells.
基金financially supported by the Natural Science Foundation of Shandong Province(ZR2023QB235,ZR202111240183,ZR2021QF120)the Postdoctoral Science Foundation of China(2022M711956)the Taishan Scholar Program of Shandong Province(tsqnz20231216).
文摘Co_(3)O_(4)possesses both direct and indirect oxidation effects and is considered as a promising catalyst for the oxidation of 5-hydroxymethylfurfural(HMF).However,the enrichment and activation effects of Co_(3)O_(4)on OH-and HMF are weak,which limits its further application.Metal defect engineering can regulate the electronic structure,optimize the adsorption of intermediates,and improve the catalytic activity by breaking the symmetry of the material,which is rarely involved in the upgrading of biomass.In this work,we prepare Co_(3)O_(4)with metal defects and load the precious metal platinum at the defect sites(PtVco).The results of in-situ characterizatio ns,electrochemical measurements,and theoretical calculations indicate that the reduction of Co-Co coordination number and the formation of Pt-Co bond induce the decrease of electron filling in the antibonding orbitals of Co element.The resulting upward shift of the d-band center of Co combined with the characteristic adsorption of Pt species synergically enhances the enrichment and activation of organic molecules and OH species,thus exhibiting excellent HMF oxidation activity(including a lower onset potential(1.14 V)and 19 times higher current density than pure Co_(3)O_(4)at 1.35 V).In summary,this work explores the adsorption enhancement mechanism of metal defect sites modified by precious metal in detail,provides a new option for improving the HMF oxidation activity of cobalt-based materials,broadens the application field of metal defect based materials,and gives an innovative guidance for the functional utilization of metal defect sites in biomass conversion.
基金National Natural Science Foundation of China (NSFC)(52273266, U2001216)Shenzhen Science and Technology Innovation Committee (20231121102401001)+2 种基金Shenzhen Key Laboratory Project (ZDSYS201602261933302)GuangdongHong Kong-Macao Joint Laboratory on Micro-Nano Manufacturing Technology (2021LSYS004)SUSTech high level special funds (G03050K002)。
文摘Widely used spin-coated nickle oxide (NiOx) based perovskite solar cells often suffer from severe interfacial reactions between the NiOxand adjacent perovskite layers due to surface defect states,which inherently impair device performance in a long-term view,even with surface molecule passivation.In this study,we developed high-quality magnetron-sputtered NiOxthin films through detailed process optimization,and compared systematically sputtered and spin-coated NiOxthin film surfaces from materials to devices.These sputtered NiOxfilms exhibit improved crystallinity,smoother surfaces,and significantly reduced Ni3+or Ni vacancies compared to their spin-coated counterparts.Consequently,the interface between the perovskite and sputtered NiOxfilm shows a substantially reduced density of defect states.Perovskite solar cells (PSCs) fabricated with our optimally sputtered NiOxfilms achieved a high power conversion efficiency (PCE) of up to 19.93%and demonstrated enhanced stability,maintaining 86.2% efficiency during 500 h of maximum power point tracking under one standard sun illumination.Moreover,with the surface modification using (4-(2,7-dibromo-9,9-dimethylacridin-10(9H)-yl)butyl)p hosphonic acid (DMAcPA),the device PCE was further promoted to 23.07%,which is the highest value reported for sputtered NiOxbased PSCs so far.
文摘Correction to:Nano-Micro Lett.(2025)17:24 https://doi.org/10.1007/s40820-024-01515-0 Following publication of the original article[1],the authors reported the author list needed to be updated because the last three author names were duplicated.The correct author list has been provided in this Correction.The original article[1]has been corrected.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea,funded by the Ministry of Education(No.2022R1I1A1A01056406)。
文摘Defective phononic crystals(PnCs)have enabled spatial localization and quantitative amplification of elastic wave energy.Most previous research has focused on applications such as narrow-bandpass filters,ultrasonic sensors,and piezoelectric energy harvesters,typically operating under the assumption of an external elastic wave incidence.Recently,a novel approach that uses defective PnCs as ultrasonic actuators to generate amplified waves has emerged.However,the existing studies are limited to the generation of either longitudinal or bending waves,with no research addressing the concurrent generation of both.Hence,this paper proposes a straightforward methodology for the concurrent generation and amplification of both wave types utilizing defect modes at independent defect-band frequencies.Bimorph piezoelectric elements are attached to the defect,with each element connected to independent external voltage sources.By precisely adjusting the magnitude and temporal phase differences between the voltage sources,concurrently amplified wave generation is achieved.The paper highlights the advantages of the proposed analytical model.This model is both computationally time-efficient and accurate,in comparison with the COMSOL simulation results.For instance,in case studies,the analytical model reduces the computational time from one hour to mere seconds,while maintaining acceptable error rates of 1%in peak frequencies.This concurrent wave-generation methodology opens new avenues for applications in rotating machinery fault diagnosis,structural health monitoring,and medical imaging.
基金funded by the National Natural Science Foundation of China(grant number 62306186)the Technology Plan Joint Foundation of Liaoning Province(grant number 2023-MSLH-246)the Technology Plan Joint Foundation of Liaoning Province(grant number 2023-BSBA-238).
文摘Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation.However,existing detection methods often struggle with challenges such as complex defect morphology,texture similarity,and fuzzy edges,leading to poor accuracy and missed detections.In order to resolve these problems,we propose MSCM-Net(Multi-Scale Cross-Modal Network),a multiscale cross-modal framework focused on detecting rail surface defects.MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps,effectively capturing and enhancing features at different scales for each modality.To further enrich feature representation and improve edge detection in blurred areas,we propose a multi-scale void fusion module that integrates multi-scale feature information.To improve cross-modal feature fusion,we develop a cross-enhanced fusion module that transfers fused features between layers to incorporate interlayer information.We also introduce a multimodal feature integration module,which merges modality-specific features from separate decoders into a shared decoder,enhancing detection by leveraging richer complementary information.Finally,we validate MSCM-Net on the NEU RSDDS-AUG RGB-depth dataset,comparing it against 12 leading methods,and the results show that MSCM-Net achieves superior performance on all metrics.
基金supported by the National Natural Science Foundation of China(51802081,62074052,and 62104061)the Natural Science Foundation of Henan Province(232300420145).
文摘The presence of SnZn-related defects in Cu_(2)ZnSn(S,Se)_(4)(CZTSSe)absorber results in large irreversible energy loss and extra irreversible electron-hole non-radiative recombination,thus hindering the efficiency enhancement of CZTSSe devices.Although the incorporation of Ag in CZTSSe can effectively suppress the SnZn-related defects and significantly improve the resulting cell performance,an excellent efficiency has not been achieved to date primarily owing to the poor electrical-conductivity and the low carrier density of the CZTSSe film induced by Ag substitution.Herein,this study exquisitely devises an Ag/H co-doping strategy in CZTSSe absorber via Ag substitution programs followed by hydrogen-plasma treatment procedure to suppress SnZn defects for achieving efficient CZTSSe devices.In-depth investigation results demonstrate that the incorporation of H in Ag-based CZTSSe absorber is expected to improve the poor electrical-conductivity and the low carrier density caused by Ag substitution.Importantly,the C=O and O-H functional groups induced by hydrogen incorporation,serving as an electron donor,can interact with under-coordinated cations in CZTSSe material,effectively passivating the SnZn-related defects.Consequently,the incorporation of an appropriate amount of Ag/H in CZTSSe mitigates carrier non-radiative recombination,prolongs minority carrier lifetime,and thus yields a champion efficiency of 14.74%,showing its promising application in kesterite-based CZTSSe devices.
基金The authors would like to express appreciation to the National Natural Science Foundation of China(Grant No.61762067)for their financial support.
文摘The primary goal of software defect prediction (SDP) is to pinpoint code modules that are likely to contain defects, thereby enabling software quality assurance teams to strategically allocate their resources and manpower. Within-project defect prediction (WPDP) is a widely used method in SDP. Despite various improvements, current methods still face challenges such as coarse-grained prediction and ineffective handling of data drift due to differences in project distribution. To address these issues, we propose a fine-grained SDP method called DIDP (drift-immune defect prediction), based on drift-immune graph neural networks (DI-GNN). DIDP converts source code into graph representations and uses DI-GNN to mitigate data drift at the model level. It also analyses key statements leading to file defects for a more detailed SDP approach. We evaluated the performance of DIDP in WPDP by examining its file-level and statement-level accuracy compared to state-of-the-art methods, and by examining its cross-project prediction accuracy. The results of the experiment show that DIDP showed significant improvements in F1-score and Recall@Top20%LOC compared to existing methods, even with large software version changes. DIDP also performed well in cross-project SDP. Our study demonstrates that DIDP achieves impressive prediction results in WPDP, effectively mitigating data drift and accurately predicting defective files. Additionally, DIDP can rank the risk of statements in defective files, aiding developers and testers in identifying potential code issues.
基金supported by National Natural Science Foundation of China(no.62376240).
文摘Software defect prediction aims to use measurement data of code and historical defects to predict potential problems,optimize testing resources and defect management.However,current methods face challenges:(1)Coarse-grained file level detection cannot accurately locate specific defects.(2)Fine-grained line-level defect prediction methods rely solely on local information of a single line of code,failing to deeply analyze the semantic context of the code line and ignoring the heuristic impact of line-level context on the code line,making it difficult to capture the interaction between global and local information.Therefore,this paper proposes a telecontext-enhanced recursive interactive attention fusion method for line-level defect prediction(TRIA-LineDP).Firstly,using a bidirectional hierarchical attention network to extract semantic features and contextual information from the original code lines as the basis.Then,the extracted contextual information is forwarded to the telecontext capture module to aggregate the global context,thereby enhancing the understanding of broader code dynamics.Finally,a recursive interaction model is used to simulate the interaction between code lines and line-level context,passing information layer by layer to enhance local and global information exchange,thereby achieving accurate defect localization.Experimental results from within-project defect prediction(WPDP)and cross-project defect prediction(CPDP)conducted on nine different projects(encompassing a total of 32 versions)demonstrated that,within the same project,the proposed methods will respectively recall at top 20%of lines of code(Recall@Top20%LOC)and effort at top 20%recall(Effort@Top20%Recall)has increased by 11%–52%and 23%–77%.In different projects,improvements of 9%–60%and 18%–77%have been achieved,which are superior to existing advanced methods and have good detection performance.
基金supported by the National Key Research and Development Program,Ministry of Science and Technology of the People's Republic of China(Grant No.2021YFC2701001)the National Natural Science Foundation of China(Grant No.81973056).
文摘Objective Exposure to polycyclic aromatic hydrocarbons(PAHs)or metal(loid)s individually has been associated with neural tube defects(NTDs).However,the impacts of PAH and metal(loid)co-exposure and potential interaction effects on NTD risk remain unclear.We conducted a case-control study in China among population with a high prevalence of NTDs to investigate the combined effects of PAH and metal(loid)exposures on the risk of NTD.Methods Cases included 80 women who gave birth to offspring with NTDs,whereas controls were 50 women who delivered infants with no congenital malformations.We analyzed the levels of placental PAHs using gas chromatography and mass spectrometry,PAH-DNA adducts with 32P-post-labeling method,and metal(loid)s with an inductively coupled plasma mass spectrometer.Unconditional logistic regression was employed to estimate the associations between individual exposures and NTDs.Least absolute shrinkage and selection operator(LASSO)penalized regression models were used to select a subset of exposures,while additive interaction models were used to identify interaction effects.Results In the single-exposure models,we found that eight PAHs,PAH-DNA adducts,and 28 metal(loid)s were associated with NTDs.Pyrene,selenium,molybdenum,cadmium,uranium,and rubidium were selected through LASSO regression and were statistically associated with NTDs in the multiple-exposure models.Women with high levels of pyrene and molybdenum or pyrene and selenium exhibited significantly increased risk of having offspring with NTDs,indicating that these combinations may have synergistic effects on the risk of NTDs.Conclusion Our findings suggest that individual PAHs and metal(loid)s,as well as their interactions,may be associated with the risk of NTDs,which warrants further investigation.
文摘Newly built tunnels often encounter a series of defects within the first few years of operation.If not promptly addressed and reinforced,these defects threaten the tunnel's durability and stability and bring severe challenges to its safe operation.This study aims to explore reinforcement techniques for addressing defects in newly built tunnels.The research begins with an analysis of common defects found in newly built tunnels,followed by a case study of the Jinfeng Tunnel in Chongqing,examining the post-construction defects.The actual reinforcement strategies and methods employed for the tunnel are then discussed.Finally,based on the research findings,this study provides insights and references for tunnel operation and construction units in China,aiming to enhance the overall quality of tunnel engineering in the country,align with sustainable development goals,and promote further improvements at a macro level.
文摘Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
