With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due ...With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.展开更多
Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rollin...Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rolling bearing faults, a prognostic algorithm consisting of four phases was proposed. Since stacked denoising auto-encoder can be filtered, noise of large numbers of mechanical vibration signals was used for deep learning structure to extract the characteristics of the noise. Unsupervised pre-training method, which can greatly simplify the traditional manual extraction approach, was utilized to process the depth of the data automatically. Furthermore, the aggregation layer of stacked denoising auto-encoder(SDA) was proposed to get rid of gradient disappearance in deeper layers of network, mix superficial nodes’ expression with deeper layers, and avoid the insufficient express ability in deeper layers. Principal component analysis(PCA) was adopted to extract different features for classification. According to the experimental data of this method and from the comparison results, the proposed method of rolling bearing fault classification reached 97.02% of correct rate, suggesting a better performance than other algorithms.展开更多
Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extra...Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models.On the other hand,skin lesionbased segregation and disintegration procedures play an essential role in earlier skin cancer detection.However,artefacts,an unclear boundary,poor contrast,and different lesion sizes make detection difficult.To address the issues in skin lesion diagnosis,this study creates the UDLS-DDOA model,an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder(UDLS)optimized by Dynamic Differential Annealed Optimization(DDOA).Pre-processing,segregation,feature removal or separation,and disintegration are part of the proposed skin lesion diagnosis model.Pre-processing of skin lesion images occurs at the initial level for noise removal in the image using the Top hat filter and painting methodology.Following that,a Fuzzy C-Means(FCM)segregation procedure is performed using a Quasi-Oppositional Elephant Herd Optimization(QOEHO)algorithm.Besides,a novel feature extraction technique using the UDLS technique is applied where the parameter tuning takes place using DDOA.In the end,the disintegration procedure would be accomplished using a SoftMax(SM)classifier.The UDLS-DDOA model is tested against the International Skin Imaging Collaboration(ISIC)dataset,and the experimental results are examined using various computational attributes.The simulation results demonstrated that the UDLS-DDOA model outperformed the compared methods significantly.展开更多
The influenza virus changes its antigenicity frequently due to rapid mutations, leading to immune escape and failure of vaccination. Rapid determination of the influenza antigenicity could help identify the antigenic ...The influenza virus changes its antigenicity frequently due to rapid mutations, leading to immune escape and failure of vaccination. Rapid determination of the influenza antigenicity could help identify the antigenic variants in time. Here, we built a stacked auto-encoder (SAE) model for predicting the antigenic variant of human influenza A(H3N2) viruses based on the hemagglutinin (HA) protein sequences. The model achieved an accuracy of 0.95 in five-fold cross-validations, better than the logistic regression model did. Further analysis of the model shows that most of the active nodes in the hidden layer reflected the combined contribution of multiple residues to antigenic variation. Besides, some features (residues on HA protein) in the input layer were observed to take part in multiple active nodes, such as residue 189, 145 and 156, which were also reported to mostly determine the antigenic variation of influenza A(H3N2) viruses. Overall,this work is not only useful for rapidly identifying antigenic variants in influenza prevention, but also an interesting attempt in inferring the mechanisms of biological process through analysis of SAE model, which may give some insights into interpretation of the deep learning展开更多
Compared to single-trait transgenic crops, stacked transgenic plants may be more prone to become weedy, and transgene flow from stacked transgenic plants to weedy relatives may pose a potential environmental risk beca...Compared to single-trait transgenic crops, stacked transgenic plants may be more prone to become weedy, and transgene flow from stacked transgenic plants to weedy relatives may pose a potential environmental risk because these hybrids could be more advantageous under specific environmental conditions. Evaluation of the potential environmental risk caused by stacked transgenes is essential for assessing the environmental consequences caused by crop-weed transgene flow. The agronomic performance of fitness-related traits was assessed in F1+(transgene positive) hybrids(using the transgenic line T1 c-19 as the paternal parent) in monoculture and mixed planting under presence or absence glufosinate pressure in the presence or absence of natural insect pressure and then compared with the performance of F1–(transgene negative) hybrids(using the non-transgenic line Minghui 63(MH63) as the paternal parent) and their weedy rice counterparts. The results demonstrated that compared with the F1– hybrids and weedy rice counterparts, the F1+ hybrid presented higher performance(P<0.05) or non-significant changes(P>0.05) under natural insect pressure, respectively, lower performance(P<0.05) or non-significant changes(P>0.05) in the absence of insect pressure in monoculture planting, respectively. And compared to weedy rice counterparts, the F1+ hybrid presented higher performance(P<0.05) or non-significant changes(P>0.05) in the presence or absence of insect pressure in mixed planting, respectively. The F1+ hybrids presented nonsignificant changes(P>0.05) under the presence or absence glufosinate pressure under insect or non-insect pressure in monoculture planting. The all F1+ hybrids and two of three F1– hybrids had significantly lower(P<0.05) seed shattering than the weedy rice counterparts. The potential risk of gene flow from T1 c-19 to weedy rice should be prevented due to the greater fitness advantage of F1 hybrids in the majority of cases.展开更多
Filtering capacitor with compact configuration and a wide range of operating voltage has been attracting increasing attention for the smooth conversion of the electric signal in modern circuits.Lossless integration of...Filtering capacitor with compact configuration and a wide range of operating voltage has been attracting increasing attention for the smooth conversion of the electric signal in modern circuits.Lossless integration of capacitor units can be regarded as one of the efficient ways to achieve a wider voltage range,which has not yet been fully conquered due to the lack of rational designs of the electrode structure and integration technology.This study presents an alternatingly stacked assemble technology to conveniently fabricate compact aqueous hybrid integrated filtering capacitors on a large scale,in which a unit consists of rGO/MXene composite film as a negative electrode and PEDOT:PSS based film as a positive electrode.Benefiting from the synergistic effect of rGO and MXene components,and morphological characteristics of PEDOT:PSS,the capacitor unit exhibits outstanding AC line filtering with a large areal specific energy density of 1,015 μF V^(2)cm^(-2)(0.28 μW h cm^(-2)) at 120 Hz.After rational integration,the assembled capacitors present compact/lightweight configuration and lossless frequency response,as reflected by almost constant resistor-capacitor time constant of 0.2 ms and dissipation factor of 15% at120 Hz,identical to those of the single capacitor unit.Apart from standing alone steadily on a flower,a small volume(only 8.1 cm^(3)) of the integrated capacitor with 70 units connected in series achieves hundred-volts alternating current line filtering,which is superior to most reported filtering capacitors with sandwich configuration.This study provides insight into the fabrication and application of compact/ultralight filtering capacitors with lossless frequency response,and a wide range of operating voltage.展开更多
The recent trend of vehicle design aims at crash safety and environmentally-friendly aspect. For the crash safety aspect, the energy absorbing members should absorb collision energy sufficiently but for the environmen...The recent trend of vehicle design aims at crash safety and environmentally-friendly aspect. For the crash safety aspect, the energy absorbing members should absorb collision energy sufficiently but for the environmentally-friendly aspect, the vehicle structure must be light weight in order to improve the fuel efficiency and reduce the tail gas emission. Therefore, the light weight of vehicle must be achieved in a securing safety status of crash. An aluminum or carbon fiber reinforced plastics (CFRP) is representative one of the light-weight materials. Based on the respective collapse behavior of aluminum and CFRP member, the collapse behavior of hybrid thin-walled member was evaluated. The hybrid members were manufactured by wrapping CFRP prepreg sheets outside the aluminum hollow members in the autoclave. Because the CFRP is an anisotropic material whose mechanical properties, such as strength and elasticity, change with its stacking condition, the effects of the stacking condition on the collapse behavior evaluation of the hybrid thin-walled member were tested. The collapse mode and energy absorption capability of the hybrid thin-walled member were analyzed with the change of the fiber orientation angle and interface number.展开更多
In order to improve the performance of whole-spacecraft vibration isolation systems,choosing piezoelectric stacks and viscoelastic material as the active and passive vibration isolation components,an innovative whole-...In order to improve the performance of whole-spacecraft vibration isolation systems,choosing piezoelectric stacks and viscoelastic material as the active and passive vibration isolation components,an innovative whole-spacecraft hybrid vibration isolation system (WSHVIS) is designed and studied.The finite element method is used to establish the dynamic model of WSHVIS and analyze its frequency response characteristic.According to the analysis results,eigensystem realization algorithm is applied to obtain the minimum-order state-space model of WSHVIS,which is used to design controller.On this basis,off-line simulation and on-line realization for the WSHVIS is performed.The simulation and experimental results showed that WSHVIS can effectively reduce the vibration loads transmitted from launch vehicle to spacecraft.Compared with passive vibration isolation system,the hybrid vibration isolation system has a significant inhibitory effect on the low-frequency vibration components,and can greatly increase the safety and reliability of spacecraft.展开更多
Twisting the stacking of layered materials leads to rich new physics. A three-dimensional topological insulator film hosts two-dimensional gapless Dirac electrons on top and bottom surfaces, which, when the film is be...Twisting the stacking of layered materials leads to rich new physics. A three-dimensional topological insulator film hosts two-dimensional gapless Dirac electrons on top and bottom surfaces, which, when the film is below some critical thickness, will hybridize and open a gap in the surface state structure. The hybridization gap can be tuned by various parameters such as film thickness and inversion symmetry, according to the literature. The three-dimensional strong topological insulator Bi(Sb)Se(Te) family has layered structures composed of quintuple layers(QLs) stacked together by van der Waals interaction. Here we successfully grow twistedly stacked Sb_2Te_3 QLs and investigate the effect of twist angels on the hybridization gaps below the thickness limit. It is found that the hybridization gap can be tuned for films of three QLs, which may lead to quantum spin Hall states.Signatures of gap-closing are found in 3-QL films. The successful in situ application of this approach opens a new route to search for exotic physics in topological insulators.展开更多
This study addresses gaps in aftershock prediction research by proposing an interpretable hybrid machine learning model that leverages multi-source data.The model overcomes challenges related to the selection of influ...This study addresses gaps in aftershock prediction research by proposing an interpretable hybrid machine learning model that leverages multi-source data.The model overcomes challenges related to the selection of influencing factors,model types,prediction result visualization,and decision mechanism interpretability.It integrates mainshock factors,geological features,site characteristics,and terrain conditions using geospatial information system(GIS)technology.By employing the stacking algorithm to optimize and combine XGBoost and LightGBM models,the proposed model significantly improves the prediction performance.Visualization through aftershock hazard mapping offers a robust tool for aftershock warning.The Shapley additive explanations(SHAP)model is used to explain the decision-making process from both global and local perspectives.Results show that,compared to the optimized XGBoost-CMA_ES and LightGBM-CMA_ES hybrid models,the stacking model achieves area under the curve(AUC)increases of 7.71%and 5.72% on the test set,respectively,with a maximum prediction accuracy of 0.9344.The hazard zoning map identifies high-risk areas mainly around fault lines and near the epicenter.As hazard levels rise,the proportion and density of aftershocks in these areas increase.The SHAP model results highlight the distance to fault as the most critical factor.The study integrates local explanations with on-site investigations,effectively visualizing the contributions of different factors to aftershocks.This research provides new tools and methods for enhancing aftershock warning and response.展开更多
The mechanical behaviour of Titanium-based Fiber Metal Laminates(FMLs)reinforced with Kevlar,Jute and the novel woven(Kevlar+Jute)fiber mat were evaluated through tensile,flexural,Charpy impact,and drop-weight tests.T...The mechanical behaviour of Titanium-based Fiber Metal Laminates(FMLs)reinforced with Kevlar,Jute and the novel woven(Kevlar+Jute)fiber mat were evaluated through tensile,flexural,Charpy impact,and drop-weight tests.The FMLs were fabricated with various stacking configurations(2/1,3/2,4/3,and 5/4)to examine their influence on mechanical properties.Kevlar-reinforced laminates consistently demonstrated superior tensile and flexural strengths,with the highest tensile strength of 772 MPa observed in the 3/2 configuration,attributed to Kevlar's excellent load-bearing capacity.Jute-reinforced laminates exhibited lower performance due to poor bonding and early delamination,while the FMLs reinforced with woven(Kevlar+Jute)fiber mat achieved a balance between mechanical strength and cost-effectiveness by attaining a tensile strength of 718 MPa in the 3/2 configuration.Impact energy absorption results revealed that Kevlar-reinforced FMLs provided the highest energy absorption under Charpy tests,reaching 13.5 J in the 3/2 configuration.The 4/3 configu ration exhibited superior resistance under drop-weight impacts,absorbing 104.7 J of energy.Failure analysis using SEM revealed key mechanisms such as fiber debonding,delamination,and fiber pull-out,with increased severity observed in laminates with a higher number of fiber-epoxy layers,especially in the 5/4 configuration.This study highlights the potential of Kevlar-Jute hybrid fiber-reinforced FMLs for applications requiring high mechanical performance and impact resistance.Future research should explore advanced surface treatments and the environmental durability of these laminates for aerospace and automotive applications.展开更多
Secure storage yard is one of the optimal core goals of container transportation;thus,making the necessary storage arrangements has become the most crucial part of the container terminal management systems（CTMS）.Thi...Secure storage yard is one of the optimal core goals of container transportation;thus,making the necessary storage arrangements has become the most crucial part of the container terminal management systems（CTMS）.This paper investigates a random hybrid stacking algorithm（RHSA） for outbound containers that randomly enter the yard.In the first stage of RHSA,the distribution among blocks was analyzed with respect to the utilization ratio.In the second stage,the optimization of bay configuration was carried out by using the hybrid genetic algorithm.Moreover,an experiment was performed to test the RHSA.The results show that the explored algorithm is useful to increase the efficiency.展开更多
Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive data.Traditional detection methods often fail to keep pace with ...Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive data.Traditional detection methods often fail to keep pace with the evolving sophistication of cyber threats.This paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack detection.Our approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in performance.It demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant loads.Furthermore,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time applications.Designed for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current defenses.This innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.展开更多
Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.Whi...Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.展开更多
Transgenic Bt corn hybrids have been available for more than 10 years and are known to control specific insects. More recently, so-called “stacked-gene” hybrids, have been released with multiple insect resistance ge...Transgenic Bt corn hybrids have been available for more than 10 years and are known to control specific insects. More recently, so-called “stacked-gene” hybrids, have been released with multiple insect resistance genes and genes for herbicide resistance, resulting in up to 6 traits per plant. Because insect damage can lead to increased levels of mycotoxins, such as aflatoxins and fumonisin, we designed a study to compare ten commercially available corn hybrids, two non-transgenic, four with both herbicide and insect tolerance (stacked-gene) and four with glyphosate tolerance only to determine if any hybrid class had the advantage of reduced mycotoxin contamination. The experiment was carried out in the Mississippi State University Delta Research Extension fields in Stoneville, MS for two years in fine sandy loam and clay soil. Rows were either inoculated at the V10 stage of growth with toxigenic Aspergillus flavus K54 (NRRL 58987, isolated from corn kernels in Mississippi), grown on wheat, and applied at a rate of 22.42 kg/ha or allowed to become naturally infected with disease-producing fungi, including various Fusarium and other Aspergillus spp. Mycotoxin production differed according to the soil type with lower levels detected in the hybrids planted in clay soil vs. sandy soil. However, no significant differences in mycotoxin production were found amongst the hybrid classes. More research is needed to identify conditions under which transgenic hybrids might produce higher yields and lower mycotoxin levels. Presently, selection of transgenic hybrids will not replace integrated strategies of biocontrol, host plant resistance, or good crop management practices for achieving adequate mycotoxin control in corn.展开更多
基金This research is supported financially by Natural Science Foundation of China(Grant No.51505234,51405241,51575283).
文摘With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.
基金Sponsored by the National Natural Science Foundation of China(Grant No.51704138)
文摘Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rolling bearing faults, a prognostic algorithm consisting of four phases was proposed. Since stacked denoising auto-encoder can be filtered, noise of large numbers of mechanical vibration signals was used for deep learning structure to extract the characteristics of the noise. Unsupervised pre-training method, which can greatly simplify the traditional manual extraction approach, was utilized to process the depth of the data automatically. Furthermore, the aggregation layer of stacked denoising auto-encoder(SDA) was proposed to get rid of gradient disappearance in deeper layers of network, mix superficial nodes’ expression with deeper layers, and avoid the insufficient express ability in deeper layers. Principal component analysis(PCA) was adopted to extract different features for classification. According to the experimental data of this method and from the comparison results, the proposed method of rolling bearing fault classification reached 97.02% of correct rate, suggesting a better performance than other algorithms.
基金deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,for funding this research work through Project Number (IFP-2020-133).
文摘Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models.On the other hand,skin lesionbased segregation and disintegration procedures play an essential role in earlier skin cancer detection.However,artefacts,an unclear boundary,poor contrast,and different lesion sizes make detection difficult.To address the issues in skin lesion diagnosis,this study creates the UDLS-DDOA model,an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder(UDLS)optimized by Dynamic Differential Annealed Optimization(DDOA).Pre-processing,segregation,feature removal or separation,and disintegration are part of the proposed skin lesion diagnosis model.Pre-processing of skin lesion images occurs at the initial level for noise removal in the image using the Top hat filter and painting methodology.Following that,a Fuzzy C-Means(FCM)segregation procedure is performed using a Quasi-Oppositional Elephant Herd Optimization(QOEHO)algorithm.Besides,a novel feature extraction technique using the UDLS technique is applied where the parameter tuning takes place using DDOA.In the end,the disintegration procedure would be accomplished using a SoftMax(SM)classifier.The UDLS-DDOA model is tested against the International Skin Imaging Collaboration(ISIC)dataset,and the experimental results are examined using various computational attributes.The simulation results demonstrated that the UDLS-DDOA model outperformed the compared methods significantly.
文摘The influenza virus changes its antigenicity frequently due to rapid mutations, leading to immune escape and failure of vaccination. Rapid determination of the influenza antigenicity could help identify the antigenic variants in time. Here, we built a stacked auto-encoder (SAE) model for predicting the antigenic variant of human influenza A(H3N2) viruses based on the hemagglutinin (HA) protein sequences. The model achieved an accuracy of 0.95 in five-fold cross-validations, better than the logistic regression model did. Further analysis of the model shows that most of the active nodes in the hidden layer reflected the combined contribution of multiple residues to antigenic variation. Besides, some features (residues on HA protein) in the input layer were observed to take part in multiple active nodes, such as residue 189, 145 and 156, which were also reported to mostly determine the antigenic variation of influenza A(H3N2) viruses. Overall,this work is not only useful for rapidly identifying antigenic variants in influenza prevention, but also an interesting attempt in inferring the mechanisms of biological process through analysis of SAE model, which may give some insights into interpretation of the deep learning
基金financially supported by the China Transgenic Organism Research and Commercialization Project (2016ZX08011-001)
文摘Compared to single-trait transgenic crops, stacked transgenic plants may be more prone to become weedy, and transgene flow from stacked transgenic plants to weedy relatives may pose a potential environmental risk because these hybrids could be more advantageous under specific environmental conditions. Evaluation of the potential environmental risk caused by stacked transgenes is essential for assessing the environmental consequences caused by crop-weed transgene flow. The agronomic performance of fitness-related traits was assessed in F1+(transgene positive) hybrids(using the transgenic line T1 c-19 as the paternal parent) in monoculture and mixed planting under presence or absence glufosinate pressure in the presence or absence of natural insect pressure and then compared with the performance of F1–(transgene negative) hybrids(using the non-transgenic line Minghui 63(MH63) as the paternal parent) and their weedy rice counterparts. The results demonstrated that compared with the F1– hybrids and weedy rice counterparts, the F1+ hybrid presented higher performance(P<0.05) or non-significant changes(P>0.05) under natural insect pressure, respectively, lower performance(P<0.05) or non-significant changes(P>0.05) in the absence of insect pressure in monoculture planting, respectively. And compared to weedy rice counterparts, the F1+ hybrid presented higher performance(P<0.05) or non-significant changes(P>0.05) in the presence or absence of insect pressure in mixed planting, respectively. The F1+ hybrids presented nonsignificant changes(P>0.05) under the presence or absence glufosinate pressure under insect or non-insect pressure in monoculture planting. The all F1+ hybrids and two of three F1– hybrids had significantly lower(P<0.05) seed shattering than the weedy rice counterparts. The potential risk of gene flow from T1 c-19 to weedy rice should be prevented due to the greater fitness advantage of F1 hybrids in the majority of cases.
基金supported by the NSFC(21805072,22075019,22035005)the National Key R&D Program of China(2017YFB1104300)。
文摘Filtering capacitor with compact configuration and a wide range of operating voltage has been attracting increasing attention for the smooth conversion of the electric signal in modern circuits.Lossless integration of capacitor units can be regarded as one of the efficient ways to achieve a wider voltage range,which has not yet been fully conquered due to the lack of rational designs of the electrode structure and integration technology.This study presents an alternatingly stacked assemble technology to conveniently fabricate compact aqueous hybrid integrated filtering capacitors on a large scale,in which a unit consists of rGO/MXene composite film as a negative electrode and PEDOT:PSS based film as a positive electrode.Benefiting from the synergistic effect of rGO and MXene components,and morphological characteristics of PEDOT:PSS,the capacitor unit exhibits outstanding AC line filtering with a large areal specific energy density of 1,015 μF V^(2)cm^(-2)(0.28 μW h cm^(-2)) at 120 Hz.After rational integration,the assembled capacitors present compact/lightweight configuration and lossless frequency response,as reflected by almost constant resistor-capacitor time constant of 0.2 ms and dissipation factor of 15% at120 Hz,identical to those of the single capacitor unit.Apart from standing alone steadily on a flower,a small volume(only 8.1 cm^(3)) of the integrated capacitor with 70 units connected in series achieves hundred-volts alternating current line filtering,which is superior to most reported filtering capacitors with sandwich configuration.This study provides insight into the fabrication and application of compact/ultralight filtering capacitors with lossless frequency response,and a wide range of operating voltage.
文摘The recent trend of vehicle design aims at crash safety and environmentally-friendly aspect. For the crash safety aspect, the energy absorbing members should absorb collision energy sufficiently but for the environmentally-friendly aspect, the vehicle structure must be light weight in order to improve the fuel efficiency and reduce the tail gas emission. Therefore, the light weight of vehicle must be achieved in a securing safety status of crash. An aluminum or carbon fiber reinforced plastics (CFRP) is representative one of the light-weight materials. Based on the respective collapse behavior of aluminum and CFRP member, the collapse behavior of hybrid thin-walled member was evaluated. The hybrid members were manufactured by wrapping CFRP prepreg sheets outside the aluminum hollow members in the autoclave. Because the CFRP is an anisotropic material whose mechanical properties, such as strength and elasticity, change with its stacking condition, the effects of the stacking condition on the collapse behavior evaluation of the hybrid thin-walled member were tested. The collapse mode and energy absorption capability of the hybrid thin-walled member were analyzed with the change of the fiber orientation angle and interface number.
基金Sponsored by the Commission of Science Technology and Industry for National Defense (Grant No.C4120062301)
文摘In order to improve the performance of whole-spacecraft vibration isolation systems,choosing piezoelectric stacks and viscoelastic material as the active and passive vibration isolation components,an innovative whole-spacecraft hybrid vibration isolation system (WSHVIS) is designed and studied.The finite element method is used to establish the dynamic model of WSHVIS and analyze its frequency response characteristic.According to the analysis results,eigensystem realization algorithm is applied to obtain the minimum-order state-space model of WSHVIS,which is used to design controller.On this basis,off-line simulation and on-line realization for the WSHVIS is performed.The simulation and experimental results showed that WSHVIS can effectively reduce the vibration loads transmitted from launch vehicle to spacecraft.Compared with passive vibration isolation system,the hybrid vibration isolation system has a significant inhibitory effect on the low-frequency vibration components,and can greatly increase the safety and reliability of spacecraft.
基金Supported by the National Natural Science Foundation of China (Grant Nos.61804056 and 92065102)。
文摘Twisting the stacking of layered materials leads to rich new physics. A three-dimensional topological insulator film hosts two-dimensional gapless Dirac electrons on top and bottom surfaces, which, when the film is below some critical thickness, will hybridize and open a gap in the surface state structure. The hybridization gap can be tuned by various parameters such as film thickness and inversion symmetry, according to the literature. The three-dimensional strong topological insulator Bi(Sb)Se(Te) family has layered structures composed of quintuple layers(QLs) stacked together by van der Waals interaction. Here we successfully grow twistedly stacked Sb_2Te_3 QLs and investigate the effect of twist angels on the hybridization gaps below the thickness limit. It is found that the hybridization gap can be tuned for films of three QLs, which may lead to quantum spin Hall states.Signatures of gap-closing are found in 3-QL films. The successful in situ application of this approach opens a new route to search for exotic physics in topological insulators.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFC3007203).
文摘This study addresses gaps in aftershock prediction research by proposing an interpretable hybrid machine learning model that leverages multi-source data.The model overcomes challenges related to the selection of influencing factors,model types,prediction result visualization,and decision mechanism interpretability.It integrates mainshock factors,geological features,site characteristics,and terrain conditions using geospatial information system(GIS)technology.By employing the stacking algorithm to optimize and combine XGBoost and LightGBM models,the proposed model significantly improves the prediction performance.Visualization through aftershock hazard mapping offers a robust tool for aftershock warning.The Shapley additive explanations(SHAP)model is used to explain the decision-making process from both global and local perspectives.Results show that,compared to the optimized XGBoost-CMA_ES and LightGBM-CMA_ES hybrid models,the stacking model achieves area under the curve(AUC)increases of 7.71%and 5.72% on the test set,respectively,with a maximum prediction accuracy of 0.9344.The hazard zoning map identifies high-risk areas mainly around fault lines and near the epicenter.As hazard levels rise,the proportion and density of aftershocks in these areas increase.The SHAP model results highlight the distance to fault as the most critical factor.The study integrates local explanations with on-site investigations,effectively visualizing the contributions of different factors to aftershocks.This research provides new tools and methods for enhancing aftershock warning and response.
基金the aid of Research and Development Fund-Seed Money provided by Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology。
文摘The mechanical behaviour of Titanium-based Fiber Metal Laminates(FMLs)reinforced with Kevlar,Jute and the novel woven(Kevlar+Jute)fiber mat were evaluated through tensile,flexural,Charpy impact,and drop-weight tests.The FMLs were fabricated with various stacking configurations(2/1,3/2,4/3,and 5/4)to examine their influence on mechanical properties.Kevlar-reinforced laminates consistently demonstrated superior tensile and flexural strengths,with the highest tensile strength of 772 MPa observed in the 3/2 configuration,attributed to Kevlar's excellent load-bearing capacity.Jute-reinforced laminates exhibited lower performance due to poor bonding and early delamination,while the FMLs reinforced with woven(Kevlar+Jute)fiber mat achieved a balance between mechanical strength and cost-effectiveness by attaining a tensile strength of 718 MPa in the 3/2 configuration.Impact energy absorption results revealed that Kevlar-reinforced FMLs provided the highest energy absorption under Charpy tests,reaching 13.5 J in the 3/2 configuration.The 4/3 configu ration exhibited superior resistance under drop-weight impacts,absorbing 104.7 J of energy.Failure analysis using SEM revealed key mechanisms such as fiber debonding,delamination,and fiber pull-out,with increased severity observed in laminates with a higher number of fiber-epoxy layers,especially in the 5/4 configuration.This study highlights the potential of Kevlar-Jute hybrid fiber-reinforced FMLs for applications requiring high mechanical performance and impact resistance.Future research should explore advanced surface treatments and the environmental durability of these laminates for aerospace and automotive applications.
基金Supported by the Research Grants from Shanghai Municipal Natural Science Foundation（No.10190502500） Shanghai Maritime University Start-up Funds,Shanghai Science＆Technology Commission Projects（No.09DZ2250400） Shanghai Education Commission Project（No.J50604）
文摘Secure storage yard is one of the optimal core goals of container transportation;thus,making the necessary storage arrangements has become the most crucial part of the container terminal management systems（CTMS）.This paper investigates a random hybrid stacking algorithm（RHSA） for outbound containers that randomly enter the yard.In the first stage of RHSA,the distribution among blocks was analyzed with respect to the utilization ratio.In the second stage,the optimization of bay configuration was carried out by using the hybrid genetic algorithm.Moreover,an experiment was performed to test the RHSA.The results show that the explored algorithm is useful to increase the efficiency.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R513),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive data.Traditional detection methods often fail to keep pace with the evolving sophistication of cyber threats.This paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack detection.Our approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in performance.It demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant loads.Furthermore,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time applications.Designed for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current defenses.This innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.
基金funding this work through the Research Group Program under the Grant Number:(R.G.P.2/382/44).
文摘Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.
文摘Transgenic Bt corn hybrids have been available for more than 10 years and are known to control specific insects. More recently, so-called “stacked-gene” hybrids, have been released with multiple insect resistance genes and genes for herbicide resistance, resulting in up to 6 traits per plant. Because insect damage can lead to increased levels of mycotoxins, such as aflatoxins and fumonisin, we designed a study to compare ten commercially available corn hybrids, two non-transgenic, four with both herbicide and insect tolerance (stacked-gene) and four with glyphosate tolerance only to determine if any hybrid class had the advantage of reduced mycotoxin contamination. The experiment was carried out in the Mississippi State University Delta Research Extension fields in Stoneville, MS for two years in fine sandy loam and clay soil. Rows were either inoculated at the V10 stage of growth with toxigenic Aspergillus flavus K54 (NRRL 58987, isolated from corn kernels in Mississippi), grown on wheat, and applied at a rate of 22.42 kg/ha or allowed to become naturally infected with disease-producing fungi, including various Fusarium and other Aspergillus spp. Mycotoxin production differed according to the soil type with lower levels detected in the hybrids planted in clay soil vs. sandy soil. However, no significant differences in mycotoxin production were found amongst the hybrid classes. More research is needed to identify conditions under which transgenic hybrids might produce higher yields and lower mycotoxin levels. Presently, selection of transgenic hybrids will not replace integrated strategies of biocontrol, host plant resistance, or good crop management practices for achieving adequate mycotoxin control in corn.
