Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy o...Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.展开更多
MacCormack explicit scheme and Baldwin-Lomax algebraic turbulent model are employed to solve the axisymmetric compressible Navier-Stokes equations for the numerical simulation of the supersonic mustanl floats interact...MacCormack explicit scheme and Baldwin-Lomax algebraic turbulent model are employed to solve the axisymmetric compressible Navier-Stokes equations for the numerical simulation of the supersonic mustanl floats interacted with transverse injection at the base of a cone. A temperature switch function must be added to the artificial viscous model suggested by jameson etc to enhance the scheme's ability to eliminate oscillation for some injection case.The typical code optimization techniques about vectorization and some useful concepts and terminology about multiprocessing of YH-2 parallel supercmputer is given and explatined with some examples After reconstruction and optimization the code gets a spedup 5 .973 on pipeline computer YH- 1 and gets a speedup 1 886 for 2 processors and 3.545 for 4 processors on YH-2 parallel supeercomputer by using domain decomposition method..展开更多
The explosive growth of social media means portrait editing and retouching are in high demand.While portraits are commonly captured and stored as raster images,editing raster images is non-trivial and requires the use...The explosive growth of social media means portrait editing and retouching are in high demand.While portraits are commonly captured and stored as raster images,editing raster images is non-trivial and requires the user to be highly skilled.Aiming at developing intuitive and easy-to-use portrait editing tools,we propose a novel vectorization method that can automatically convert raster images into a 3-tier hierarchical representation.The base layer consists of a set of sparse diffusion curves(DCs)which characterize salient geometric features and low-frequency colors,providing a means for semantic color transfer and facial expression editing.The middle level encodes specular highlights and shadows as large,editable Poisson regions(PRs)and allows the user to directly adjust illumination by tuning the strength and changing the shapes of PRs.The top level contains two types of pixel-sized PRs for high-frequency residuals and fine details such as pimples and pigmentation.We train a deep generative model that can produce high-frequency residuals automatically.Thanks to the inherent meaning in vector primitives,editing portraits becomes easy and intuitive.In particular,our method supports color transfer,facial expression editing,highlight and shadow editing,and automatic retouching.To quantitatively evaluate the results,we extend the commonly used FLIP metric(which measures color and feature differences between two images)to consider illumination.The new metric,illumination-sensitive FLIP,can effectively capture salient changes in color transfer results,and is more consistent with human perception than FLIP and other quality measures for portrait images.We evaluate our method on the FFHQR dataset and show it to be effective for common portrait editing tasks,such as retouching,light editing,color transfer,and expression editing.展开更多
In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot al...In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.展开更多
Vector graphics plays an important role in computer animation and imaging technologies. However present techniques and tools cannot fully replace traditional pencil and paper. Additionally, vector representation of an...Vector graphics plays an important role in computer animation and imaging technologies. However present techniques and tools cannot fully replace traditional pencil and paper. Additionally, vector representation of an image is not always available. There is not yet a good solution for vectorizing a picture drawn on a paper. This work attempts to solve the problem of vectorizing grayscale line drawings. The solution proposed uses Disk B-Spline curves to represent strokes of an image in vector form. The algorithm builds a vector representation from a grayscale raster image, which can be a scanned picture for instance. The proposed method uses a Gaussian sliding window to calculate skeleton and perceptive width of a stroke. As a result of vectorization, the given image is represented by a set of Disk B-Spline curves.展开更多
This study presents an innovative approach to calculating the failure probability of slopes by incorporating fuzzylimit-state functions,a method that significantly enhances the accuracy and efficiency of slope stabili...This study presents an innovative approach to calculating the failure probability of slopes by incorporating fuzzylimit-state functions,a method that significantly enhances the accuracy and efficiency of slope stability analysis.Unlike traditional probabilistic techniques,this approach utilizes a least squares support vector machine(LSSVM)optimized with a grey wolf optimizer(GWO)and K-fold cross-validation(CV)to approximate the limit-statefunction,thus reducing computational complexity.The novelty of this work lies in its application to one-dimensional(1D),two-dimensional(2D),and three-dimensional(3D)slope models,demonstrating its versatility andhigh precision.The proposed method consistently achieves error margins within 3%of Monte Carlo simulation(MCS)results,while substantially reducing computation time,particularly for 2D and 3D models.This makes theapproach highly practical for real-world engineering applications.Furthermore,by applying fuzzy mathematics tohandle uncertainties in geotechnical properties,the method offers a more realistic and comprehensive understandingof slope stability.As water is the main factor influencing the stability of slopes,this aspect is investigatedby calculating the phreatic line after the change in water level.Relevant examples are used to show that the failureprobability of a slope under water wading condition can increase by more than 20%(increase rates in 1D,2D and3D conditions being 25%,27%and 31%,respectively)compared with the natural condition.The influence ofdiverse fuzzy membership functions—linear,normal,and Cauchy—on failure probability is also considered.Thisresearch not only provides a strategy for better calculation of the slope failure probability but also pioneers theintegration of computational intelligence,fuzzy logic and fluid-dynamics in geotechnical engineering,presentingan innovative and efficient tool for slope stability analysis.展开更多
In this paper, the solution of the matrix second semi-tensor product equation A∘lX∘lB=Cis studied. Firstly, the solvability of the matrix-vector second semi-tensor product equation is investigated. At the same time,...In this paper, the solution of the matrix second semi-tensor product equation A∘lX∘lB=Cis studied. Firstly, the solvability of the matrix-vector second semi-tensor product equation is investigated. At the same time, the compatibility conditions, the sufficient and necessary conditions and the specific solution methods for the matrix solution are given. Secondly, we further consider the solvability of the second semi-tensor product equation of the matrix. For each part, several examples are given to illustrate the validity of the results.展开更多
The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important prac...The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.展开更多
The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques we...The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.展开更多
Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, f...Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.展开更多
Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study intr...Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.展开更多
In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selec...In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.展开更多
Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for...Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.展开更多
Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR ...Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.展开更多
Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detectio...Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.展开更多
Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework...Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.展开更多
Our previous studies have successfully grafted biotin and galactose onto chitosan(CS)and synthesized biotin modified galactosylated chitosan(Bio-GC).The optimum N/P ratio of Bio-GC and plasmid DNA was 3:1.At this N/P ...Our previous studies have successfully grafted biotin and galactose onto chitosan(CS)and synthesized biotin modified galactosylated chitosan(Bio-GC).The optimum N/P ratio of Bio-GC and plasmid DNA was 3:1.At this N/P ratio,the transfection efficiency in the hepatoma cells was the highest with a slow release effect.Bio-GC nanomaterials exhibit the protective effect of preventing the gene from nuclease degradation,and can target the transfection into hepatoma cells by combination with galactose and biotin receptors.The transfection rate was inhibited by the competition of galactose and biotin.Bio-GC nanomaterials were imported into cells’cytoplasm by their receptors,followed by the imported exogenous gene transfected into the cells.Bio-GC nanomaterials can also cause inhibitory activity in the hepatoma cells in the model of orthotopic liver transplantation in mice,by carrying the gene through the blood to the hepatoma tissue.Taken together,bio-GC nanomaterials act as gene vectors with the activity of protecting the gene from DNase degradation,improving the rate of transfection in hepatoma cells,and transporting the gene into the cytoplasm in vitro and in vivo.Therefore,they are efficient hepatoma-targeting gene carriers.展开更多
To consider the complex soil-structure interaction in a pile-slope system,it is necessary to analyze the performance of pile-slope systems based on a three-dimensional(3D)numerical model.Reliability analysis of a pile...To consider the complex soil-structure interaction in a pile-slope system,it is necessary to analyze the performance of pile-slope systems based on a three-dimensional(3D)numerical model.Reliability analysis of a pile-slope system based on 3D numerical modeling is very challenging because it is computationally expensive and the performance function of the pile failure mode is only defined in the safe domain of soil stability.In this paper,an efficient hybrid response surface method is suggested to study the system reliability of pile-reinforced slopes,where the support vector machine and the Kriging model are used to approximate performance functions of soil failure and pile failure,respectively.The versatility of the suggested method is illustrated in detail with an example.For the example examined in this paper,it is found that the pile failure can significantly contribute to system failure,and the reinforcement ratio can effectively reduce the probability of pile failure.There exists a critical reinforcement ratio beyond which the system failure probability is not sensitive to the reinforcement ratio.The pile spacing affects both the probabilities of soil failure and pile failure of the pile-reinforced slope.There exists an optimal location and an optimal length for the stabilizing piles.展开更多
Objective:To determine the current insecticide resistance status of Aedes(Ae.)aegypti and Ae.albopictus to four insecticides,namely 0.05%deltamethrin,0.75%permethrin,5%malathion and 0.25%pirimiphos-methyl using the Wo...Objective:To determine the current insecticide resistance status of Aedes(Ae.)aegypti and Ae.albopictus to four insecticides,namely 0.05%deltamethrin,0.75%permethrin,5%malathion and 0.25%pirimiphos-methyl using the World Health Organisation(WHO)susceptibility test kit.Methods:Adult bioassays were carried out using the standard protocol of the World Health Organisation.All F1 generation urban and suburban field strains of Ae.aegypti and Ae.albopictus were tested against pyrethroid and organophosphate insecticides,including the presence of piperonyl butoxide(PBO)in four replicates of 25 non-blood-fed female mosquitoes ranging from 3 to 5 days old.The Vector Control Research Unit(VCRU)laboratory strain served as a reference strain.Results:In this study,0.05%deltamethrin demonstrated a lower value of knockdown time when 50%of the mosquito population died(KT50)and knockdown time when 95%of the mosquito population died(KT95),which is significantly more effective compared to 0.75%permethrin against adult female Ae.aegypti(urban and suburban)and Ae.albopictus(urban and suburban)(ANOVA,P<0.01).Meanwhile,5%malathion was a more effective insecticide,amounting to the shorter KT50 and KT95 compared to 0.25%pirimiphos-methyl against Ae.aegypti(urban and suburban)and Ae.albopictus(urban and suburban).Ae.aegypti urban and Ae.aegypti suburban performed a higher resistance ratio(RR)towards both 0.05%deltamethrin and 0.75%permethrin due to the wide use of permethrin in dengue vector control programs in Malaysia.However,Ae.albopictus urban and suburban have lower resistance than Ae.aegypti urban and suburban towards 0.05%deltamethrin and 0.75%permethrin at 24 hours post-treatment.The addition of PBO with these insecticides successfully reduced knockdown time(KT50 and KT95)values of most of the Ae.aegypti and Ae.albopictus field strains except PBO+0.75%permethrin against Ae.aegypti suburban.Conclusions:The addition of PBO to insecticides has significantly reduced the knockdown time(KT50 and KT95)values on most of Ae.aegypti and Ae.albopictus urban strain except PBO+5%malathion against Ae.albopictus urban strain and PBO+0.75%permethrin against Ae.albopictus suburban strain in comparison to exposure to insecticides without PBO.Ae.aegypti showed a higher resistance ratio of 50(RR50)when compared with the VCRU laboratory reference strain(susceptible strain)at the exposure to the deltamethrin,including with pre-exposure to PBO.This study found that the addition of PBO with organophosphates(5%malathion and 0.25%pirimiphos-methyl)was significantly more effective than pyrethroids against Ae.aegypti and Ae.albopictus(urban and suburban)due to their high mortality rate at 24 hours.It can be concluded that the usage of PBO can help reduce resistance alteration in Aedes mosquitoes.展开更多
基金Supported by the National Natural Science Foundation of China (61074153, 61104131)the Fundamental Research Fundsfor Central Universities of China (ZY1111, JD1104)
文摘Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.
文摘MacCormack explicit scheme and Baldwin-Lomax algebraic turbulent model are employed to solve the axisymmetric compressible Navier-Stokes equations for the numerical simulation of the supersonic mustanl floats interacted with transverse injection at the base of a cone. A temperature switch function must be added to the artificial viscous model suggested by jameson etc to enhance the scheme's ability to eliminate oscillation for some injection case.The typical code optimization techniques about vectorization and some useful concepts and terminology about multiprocessing of YH-2 parallel supercmputer is given and explatined with some examples After reconstruction and optimization the code gets a spedup 5 .973 on pipeline computer YH- 1 and gets a speedup 1 886 for 2 processors and 3.545 for 4 processors on YH-2 parallel supeercomputer by using domain decomposition method..
基金This project was supported by the Ministry of Education,Singapore,under its Academic Research Fund Tier 1(RG20/20)the National Natural Science Foundation of China(61872347)the Special Plan for the Development of Distinguished Young Scientists of ISCAS(Y8RC535018).
文摘The explosive growth of social media means portrait editing and retouching are in high demand.While portraits are commonly captured and stored as raster images,editing raster images is non-trivial and requires the user to be highly skilled.Aiming at developing intuitive and easy-to-use portrait editing tools,we propose a novel vectorization method that can automatically convert raster images into a 3-tier hierarchical representation.The base layer consists of a set of sparse diffusion curves(DCs)which characterize salient geometric features and low-frequency colors,providing a means for semantic color transfer and facial expression editing.The middle level encodes specular highlights and shadows as large,editable Poisson regions(PRs)and allows the user to directly adjust illumination by tuning the strength and changing the shapes of PRs.The top level contains two types of pixel-sized PRs for high-frequency residuals and fine details such as pimples and pigmentation.We train a deep generative model that can produce high-frequency residuals automatically.Thanks to the inherent meaning in vector primitives,editing portraits becomes easy and intuitive.In particular,our method supports color transfer,facial expression editing,highlight and shadow editing,and automatic retouching.To quantitatively evaluate the results,we extend the commonly used FLIP metric(which measures color and feature differences between two images)to consider illumination.The new metric,illumination-sensitive FLIP,can effectively capture salient changes in color transfer results,and is more consistent with human perception than FLIP and other quality measures for portrait images.We evaluate our method on the FFHQR dataset and show it to be effective for common portrait editing tasks,such as retouching,light editing,color transfer,and expression editing.
基金supported by the SP2024/089 Project by the Faculty of Materials Science and Technology,VˇSB-Technical University of Ostrava.
文摘In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.
文摘Vector graphics plays an important role in computer animation and imaging technologies. However present techniques and tools cannot fully replace traditional pencil and paper. Additionally, vector representation of an image is not always available. There is not yet a good solution for vectorizing a picture drawn on a paper. This work attempts to solve the problem of vectorizing grayscale line drawings. The solution proposed uses Disk B-Spline curves to represent strokes of an image in vector form. The algorithm builds a vector representation from a grayscale raster image, which can be a scanned picture for instance. The proposed method uses a Gaussian sliding window to calculate skeleton and perceptive width of a stroke. As a result of vectorization, the given image is represented by a set of Disk B-Spline curves.
基金Ministry of Education,Center for Scientific Research and Development of Higher Education Institutions“Innovative Application of Virtual Simulation Technology in Vocational Education Teaching”Special Project,Project No.ZJXF2022110.
文摘This study presents an innovative approach to calculating the failure probability of slopes by incorporating fuzzylimit-state functions,a method that significantly enhances the accuracy and efficiency of slope stability analysis.Unlike traditional probabilistic techniques,this approach utilizes a least squares support vector machine(LSSVM)optimized with a grey wolf optimizer(GWO)and K-fold cross-validation(CV)to approximate the limit-statefunction,thus reducing computational complexity.The novelty of this work lies in its application to one-dimensional(1D),two-dimensional(2D),and three-dimensional(3D)slope models,demonstrating its versatility andhigh precision.The proposed method consistently achieves error margins within 3%of Monte Carlo simulation(MCS)results,while substantially reducing computation time,particularly for 2D and 3D models.This makes theapproach highly practical for real-world engineering applications.Furthermore,by applying fuzzy mathematics tohandle uncertainties in geotechnical properties,the method offers a more realistic and comprehensive understandingof slope stability.As water is the main factor influencing the stability of slopes,this aspect is investigatedby calculating the phreatic line after the change in water level.Relevant examples are used to show that the failureprobability of a slope under water wading condition can increase by more than 20%(increase rates in 1D,2D and3D conditions being 25%,27%and 31%,respectively)compared with the natural condition.The influence ofdiverse fuzzy membership functions—linear,normal,and Cauchy—on failure probability is also considered.Thisresearch not only provides a strategy for better calculation of the slope failure probability but also pioneers theintegration of computational intelligence,fuzzy logic and fluid-dynamics in geotechnical engineering,presentingan innovative and efficient tool for slope stability analysis.
文摘In this paper, the solution of the matrix second semi-tensor product equation A∘lX∘lB=Cis studied. Firstly, the solvability of the matrix-vector second semi-tensor product equation is investigated. At the same time, the compatibility conditions, the sufficient and necessary conditions and the specific solution methods for the matrix solution are given. Secondly, we further consider the solvability of the second semi-tensor product equation of the matrix. For each part, several examples are given to illustrate the validity of the results.
基金financially supported by the National Natural Science Foundation of China(No.51974028)。
文摘The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(Grant no.2019QZKK0904)Natural Science Foundation of Hebei Province(Grant no.D2022403032)S&T Program of Hebei(Grant no.E2021403001).
文摘The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.
基金the Competitive Research Fund of the University of Aizu,Japan.
文摘Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.
基金funded by the National Science and Technology Council,Taiwan(Grant No.NSTC 112-2121-M-039-001)by China Medical University(Grant No.CMU112-MF-79).
文摘Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.
基金the Deputyship for Research and Innovation,“Ministry of Education”in Saudi Arabia for funding this research(IFKSUOR3-014-3).
文摘In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.
基金financially supported by the National Council for Scientific and Technological Development(CNPq,Brazil),Swedish-Brazilian Research and Innovation Centre(CISB),and Saab AB under Grant No.CNPq:200053/2022-1the National Council for Scientific and Technological Development(CNPq,Brazil)under Grants No.CNPq:312924/2017-8 and No.CNPq:314660/2020-8.
文摘Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.
基金This research was funded by the National Natural Science Foundation of China(Nos.71762010,62262019,62162025,61966013,12162012)the Hainan Provincial Natural Science Foundation of China(Nos.823RC488,623RC481,620RC603,621QN241,620RC602,121RC536)+1 种基金the Haikou Science and Technology Plan Project of China(No.2022-016)the Project supported by the Education Department of Hainan Province,No.Hnky2021-23.
文摘Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.
基金the National Natural Science Foundation of China(Grant Nos.62272478,62202496,61872384).
文摘Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.
文摘Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.
基金Funded by the Scientific Research Project of Shanghai Municipal Health Commission(No.201940430)。
文摘Our previous studies have successfully grafted biotin and galactose onto chitosan(CS)and synthesized biotin modified galactosylated chitosan(Bio-GC).The optimum N/P ratio of Bio-GC and plasmid DNA was 3:1.At this N/P ratio,the transfection efficiency in the hepatoma cells was the highest with a slow release effect.Bio-GC nanomaterials exhibit the protective effect of preventing the gene from nuclease degradation,and can target the transfection into hepatoma cells by combination with galactose and biotin receptors.The transfection rate was inhibited by the competition of galactose and biotin.Bio-GC nanomaterials were imported into cells’cytoplasm by their receptors,followed by the imported exogenous gene transfected into the cells.Bio-GC nanomaterials can also cause inhibitory activity in the hepatoma cells in the model of orthotopic liver transplantation in mice,by carrying the gene through the blood to the hepatoma tissue.Taken together,bio-GC nanomaterials act as gene vectors with the activity of protecting the gene from DNase degradation,improving the rate of transfection in hepatoma cells,and transporting the gene into the cytoplasm in vitro and in vivo.Therefore,they are efficient hepatoma-targeting gene carriers.
基金substantially supported by the National Natural Science Foundation of China(Grant No.42072302)Shuguang Program from Shanghai Education Development Foundation and Shanghai Municipal Education Commission(Grant No.19SG19)Fundamental Research Funds for the Central Universities.
文摘To consider the complex soil-structure interaction in a pile-slope system,it is necessary to analyze the performance of pile-slope systems based on a three-dimensional(3D)numerical model.Reliability analysis of a pile-slope system based on 3D numerical modeling is very challenging because it is computationally expensive and the performance function of the pile failure mode is only defined in the safe domain of soil stability.In this paper,an efficient hybrid response surface method is suggested to study the system reliability of pile-reinforced slopes,where the support vector machine and the Kriging model are used to approximate performance functions of soil failure and pile failure,respectively.The versatility of the suggested method is illustrated in detail with an example.For the example examined in this paper,it is found that the pile failure can significantly contribute to system failure,and the reinforcement ratio can effectively reduce the probability of pile failure.There exists a critical reinforcement ratio beyond which the system failure probability is not sensitive to the reinforcement ratio.The pile spacing affects both the probabilities of soil failure and pile failure of the pile-reinforced slope.There exists an optimal location and an optimal length for the stabilizing piles.
基金the Fundamental Research Grant Scheme,Ministry of Higher Education Malaysia(FRGS/1/2023/STG03/USM/02/4).
文摘Objective:To determine the current insecticide resistance status of Aedes(Ae.)aegypti and Ae.albopictus to four insecticides,namely 0.05%deltamethrin,0.75%permethrin,5%malathion and 0.25%pirimiphos-methyl using the World Health Organisation(WHO)susceptibility test kit.Methods:Adult bioassays were carried out using the standard protocol of the World Health Organisation.All F1 generation urban and suburban field strains of Ae.aegypti and Ae.albopictus were tested against pyrethroid and organophosphate insecticides,including the presence of piperonyl butoxide(PBO)in four replicates of 25 non-blood-fed female mosquitoes ranging from 3 to 5 days old.The Vector Control Research Unit(VCRU)laboratory strain served as a reference strain.Results:In this study,0.05%deltamethrin demonstrated a lower value of knockdown time when 50%of the mosquito population died(KT50)and knockdown time when 95%of the mosquito population died(KT95),which is significantly more effective compared to 0.75%permethrin against adult female Ae.aegypti(urban and suburban)and Ae.albopictus(urban and suburban)(ANOVA,P<0.01).Meanwhile,5%malathion was a more effective insecticide,amounting to the shorter KT50 and KT95 compared to 0.25%pirimiphos-methyl against Ae.aegypti(urban and suburban)and Ae.albopictus(urban and suburban).Ae.aegypti urban and Ae.aegypti suburban performed a higher resistance ratio(RR)towards both 0.05%deltamethrin and 0.75%permethrin due to the wide use of permethrin in dengue vector control programs in Malaysia.However,Ae.albopictus urban and suburban have lower resistance than Ae.aegypti urban and suburban towards 0.05%deltamethrin and 0.75%permethrin at 24 hours post-treatment.The addition of PBO with these insecticides successfully reduced knockdown time(KT50 and KT95)values of most of the Ae.aegypti and Ae.albopictus field strains except PBO+0.75%permethrin against Ae.aegypti suburban.Conclusions:The addition of PBO to insecticides has significantly reduced the knockdown time(KT50 and KT95)values on most of Ae.aegypti and Ae.albopictus urban strain except PBO+5%malathion against Ae.albopictus urban strain and PBO+0.75%permethrin against Ae.albopictus suburban strain in comparison to exposure to insecticides without PBO.Ae.aegypti showed a higher resistance ratio of 50(RR50)when compared with the VCRU laboratory reference strain(susceptible strain)at the exposure to the deltamethrin,including with pre-exposure to PBO.This study found that the addition of PBO with organophosphates(5%malathion and 0.25%pirimiphos-methyl)was significantly more effective than pyrethroids against Ae.aegypti and Ae.albopictus(urban and suburban)due to their high mortality rate at 24 hours.It can be concluded that the usage of PBO can help reduce resistance alteration in Aedes mosquitoes.