Hybrid response surface method for system reliability analysis of pilereinforced slopes

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.

Active Fault Tolerant Nonsingular Terminal Sliding Mode Control for Electromechanical System Based on Support Vector Machine

Effective fault diagnosis and fault-tolerant control method for aeronautics electromechanical actuator is concerned in this paper.By borrowing the advantages of model-driven and data-driven methods,a fault tolerant nonsingular terminal sliding mode control method based on support vector machine(SVM)is proposed.A SVM is designed to estimate the fault by off-line learning from small sample data with solving convex quadratic programming method and is introduced into a high-gain observer,so as to improve the state estimation and fault detection accuracy when the fault occurs.The state estimation value of the observer is used for state reconfiguration.A novel nonsingular terminal sliding mode surface is designed,and Lyapunov theorem is used to derive a parameter adaptation law and a control law.It is guaranteed that the proposed controller can achieve asymptotical stability which is superior to many advanced fault-tolerant controllers.In addition,the parameter estimation also can help to diagnose the system faults because the faults can be reflected by the parameters variation.Extensive comparative simulation and experimental results illustrate the effectiveness and advancement of the proposed controller compared with several other main-stream controllers.

Antibodies elicited by Newcastle disease virus-vectored H7N9 avian influenza vaccine are functional in activating the complement system

H7N9 subtype avian influenza virus poses a great challenge for poultry industry.Newcastle disease virus(NDV)-vectored H7N9 avian influenza vaccines(NDV_(vec)H7N9)are effective in disease control because they are protective and allow mass administration.Of note,these vaccines elicit undetectable H7N9-specific hemagglutination-inhibition(HI)but high IgG antibodies in chickens.However,the molecular basis and protective mechanism underlying this particular antibody immunity remain unclear.Herein,immunization with an NDV_(vec)H7N9 induced low anti-H7N9 HI and virus neutralization titers but high levels of hemagglutinin(HA)-binding IgG antibodies in chickens.Three residues(S150,G151 and S152)in HA of H7N9 virus were identified as the dominant epitopes recognized by the NDV_(vec)H7N9 immune serum.Passively transferred NDV_(vec)H7N9 immune serum conferred complete protection against H7N9 virus infection in chickens.The NDV_(vec)H7N9 immune serum can mediate a potent lysis of HA-expressing and H7N9 virus-infected cells and significantly suppress H7N9 virus infectivity.These activities of the serum were significantly impaired after heat-inactivation or treatment with complement inhibitor,suggesting the engagement of the complement system.Moreover,mutations in the 150-SGS-152 sites in HA resulted in significant reductions in cell lysis and virus neutralization mediated by the NDV_(vec)H7N9 immune serum,indicating the requirement of antibody-antigen binding for complement activity.Therefore,antibodies induced by the NDV_(vec)H7N9 can activate antibody-dependent complement-mediated lysis of H7N9 virus-infected cells and complement-mediated neutralization of H7N9 virus.Our findings unveiled a novel role of the complement in protection conferred by the NDV_(vec)H7N9,highlighting a potential benefit of engaging the complement system in H7N9 vaccine design.

A New Method for Deriving High-Vertical-Resolution Wind Vector Data from the L-Band Radar Sounding System in China

High-vertical-resolution radiosonde wind data are highly valuable for describing the dynamics of the meso-and microscale atmosphere. However, the current algorithm used in China's L-band radar sounding system for calculating highvertical-resolution wind vectors excessively smooths the data, resulting in significant underestimation of the calculated kinetic energy of gravity waves compared to similar products from other countries, which greatly limits the effective utilization of the data. To address this issue, this study proposes a novel method to calculate high-vertical-resolution wind vectors that utilizes the elevation angle, azimuth angle, and slant range from L-band radar. In order to obtain wind data with a stable quality, a two-step automatic quality control procedure, including the RMSE-F(root-mean-square error F) test and elemental consistency test are first applied to the slant range data, to eliminate continuous erroneous data caused by unstable signals or radar malfunctions. Then, a wind calculation scheme based on a sliding second-order polynomial fitting is utilized to derive the high-vertical-resolution radiosonde wind vectors. The evaluation results demonstrate that the wind data obtained through the proposed method show a high level of consistency with the high-resolution wind data observed using the Vaisala Global Positioning System and the data observed by the new Beidou Navigation Sounding System. The calculation of the kinetic energy of gravity waves in the recalculated wind data also reaches a level comparable to the Vaisala observations.

Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

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.

A Privacy Preserving Federated Learning System for IoT Devices Using Blockchain and Optimization

In this study, a blockchain based federated learning system using an enhanced weighted mean vector optimization algorithm, known as EINFO, is proposed. The proposed EINFO addresses the limitations of federated averaging during global update and model training, where data is unevenly distributed among devices and there are variations in the number of data samples. Using a well-defined structure and updating the vector positions by local searching, vector combining, and updating rules, the EINFO algorithm maximizes the shared model parameters. In order to increase the exploration and exploitation capabilities, the model convergence rate is improved and new vectors are generated through the use of a weighted mean vector based on the inverse square law. To choose validators, miners, and to propagate new blocks, a delegated proof of stake based on the reliability of blockchain nodes is suggested. Federated learning is included into the blockchain to protect nodes from both external and internal threats. To determine how well the suggested system performs in relation to current models in the literature, extensive simulations are run. The simulation results show that the proposed system outperforms existing schemes in terms of accuracy, sensitivity and specificity.

The potential of gene therapies for spinal cord injury repair:a systematic review and meta-analysis of pre-clinical studies

Currently,there is no cure for traumatic spinal co rd injury but one therapeutic approach showing promise is gene therapy.In this systematic review and meta-analysis,we aim to assess the efficacy of gene therapies in pre-clinical models of spinal cord injury and the risk of bias.In this metaanalysis,registe red at PROSPERO(Registration ID:CRD42020185008),we identified relevant controlled in vivo studies published in English by searching the PubMed,Web of Science,and Embase databases.No restrictions of the year of publication were applied and the last literature search was conducted on August 3,2020.We then conducted a random-effects meta-analysis using the restricted maximum likelihood estimator.A total of 71 studies met our inclusion crite ria and were included in the systematic review.Our results showed that overall,gene therapies were associated with improvements in locomotor score(standardized mean difference[SMD]:2.07,95%confidence interval[CI]:1.68-2.47,Tau^(2)=2.13,I^(2)=83.6%)and axonal regrowth(SMD:2.78,95%CI:1.92-3.65,Tau^(2)=4.13,I^(2)=85.5%).There was significant asymmetry in the funnel plots of both outcome measures indicating the presence of publication bias.We used a modified CAMARADES(Collaborative Approach to M eta-Analysis and Review of Animal Data in Experimental Studies)checklist to assess the risk of bias,finding that the median score was 4(IQR:3-5).In particula r,reports of allocation concealment and sample size calculations were lacking.In conclusion,gene therapies are showing promise as therapies for spinal co rd injury repair,but there is no consensus on which gene or genes should be targeted.

Support vector regression-based operational effectiveness evaluation approach to reconnaissance satellite system

As one of the most important part of weapon system of systems(WSoS),quantitative evaluation of reconnaissance satellite system(RSS)is indispensable during its construction and application.Aiming at the problem of nonlinear effectiveness evaluation under small sample conditions,we propose an evaluation method based on support vector regression(SVR)to effectively address the defects of traditional methods.Considering the performance of SVR is influenced by the penalty factor,kernel type,and other parameters deeply,the improved grey wolf optimizer(IGWO)is employed for parameter optimization.In the proposed IGWO algorithm,the opposition-based learning strategy is adopted to increase the probability of avoiding the local optima,the mutation operator is used to escape from premature convergence and differential convergence factors are applied to increase the rate of convergence.Numerical experiments of 14 test functions validate the applicability of IGWO algorithm dealing with global optimization.The index system and evaluation method are constructed based on the characteristics of RSS.To validate the proposed IGWO-SVR evaluation method,eight benchmark data sets and combat simulation are employed to estimate the evaluation accuracy,convergence performance and computational complexity.According to the experimental results,the proposed method outperforms several prediction based evaluation methods,verifies the superiority and effectiveness in RSS operational effectiveness evaluation.

CRISPR/Cas9 systems:Delivery technologies and biomedical applications

The emergence of the clustered regularly interspaced short palindromic repeats(CRISPR)/CRISPR-associated protein 9(Cas9)genome-editing system has brought about a significant revolution in the realm of managing human diseases,establishing animal models,and so on.To fully harness the potential of this potent gene-editing tool,ensuring efficient and secure delivery to the target site is paramount.Consequently,developing effective delivery methods for the CRISPR/Cas9 system has become a critical area of research.In this review,we present a comprehensive outline of delivery strategies and discuss their biomedical applications in the CRISPR/Cas9 system.We also provide an indepth analysis of physical,viral vector,and non-viral vector delivery strategies,including plasmid-,mRNA-and protein-based approach.In addition,we illustrate the biomedical applications of the CRISPR/Cas9 system.This review highlights the key factors affecting the delivery process and the current challenges facing the CRISPR/Cas9 system,while also delineating future directions and prospects that could inspire innovative delivery strategies.This review aims to provide new insights and ideas for advancing CRISPR/Cas9-based delivery strategies and to facilitate breakthroughs in biomedical research and therapeutic applications.

Insights on drug and gene delivery systems in liver fibrosis

Complications of the liver are amongst the world’s worst diseases.Liver fibrosis is the first stage of liver problems,while cirrhosis is the last stage,which can lead to death.The creation of effective anti-fibrotic drug delivery methods appears critical due to the liver’s metabolic capacity for drugs and the presence of insurmountable physiological impediments in the way of targeting.Recent breakthroughs in anti-fibrotic agents have substantially assisted in fibrosis;nevertheless,the working mechanism of anti-fibrotic medications is not fully understood,and there is a need to design delivery systems that are well-understood and can aid in cirrhosis.Nanotechnology-based delivery systems are regarded to be effective but they have not been adequately researched for liver delivery.As a result,the capability of nanoparticles in hepatic delivery was explored.Another approach is targeted drug delivery,which can considerably improve efficacy if delivery systems are designed to target hepatic stellate cells(HSCs).We have addressed numerous delivery strategies that target HSCs,which can eventually aid in fibrosis.Recently genetics have proved to be useful,and methods for delivering genetic material to the target place have also been investigated where different techniques are depicted.To summarize,this review paper sheds light on themost recent breakthroughs in drug and gene-based nano and targeted delivery systems that have lately shown useful for the treatment of liver fibrosis and cirrhosis.

A new hybrid approach to assessing soil quality using neutrosophic fuzzy-AHP and support vector machine algorithm in sub-humid ecosystem

Soil quality determination and estimation is an important issue not only for terrestrial ecosystems but also for sustainable management of soils.In this study,soil quality was determined by linear and nonlinear standard scoring function methods integrated with a neutrosophic fuzzy analytic hierarchy process in the micro catchment.In addition,soil quality values were estimated using a support vector machine(SVM)in machine learning algorithms.In order to generate spatial distribution maps of soil quality indice values,different interpolation methods were evaluated to detect the most suitable semivariogram model.While the soil quality index values obtained by the linear method were determined between 0.458-0.717,the soil quality index with the nonlinear method showed variability at the levels of 0.433-0.651.There was no statistical difference between the two methods,and they were determined to be similar.In the estimation of soil quality with SVM,the normalized root means square error(NRMSE)values obtained in the linear and nonlinear method estimation were determined as 0.057 and 0.047,respectively.The spherical model of simple kriging was determined as the interpolation method with the lowest RMSE value in the actual and predicted values of the linear method while,in the nonlinear method,the lowest error in the distribution maps was determined with exponential of the simple kriging.

DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of DNA.Vector quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency.This article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage Systems.The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage.Besides,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded form.In addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably minimized.In order to generate optimal codebook for LBG,the WSA is applied to it.The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures.The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

An Effective Diagnosis System for Brain Tumor Detection and Classification

A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain.This growth is considered deadly since it may cause death.The brain controls numerous functions,such as memory,vision,and emotions.Due to the location,size,and shape of these tumors,their detection is a challenging and complex task.Several efforts have been conducted toward improved detection and yielded promising results and outcomes.However,the accuracy should be higher than what has been reached.This paper presents a method to detect brain tumors with high accuracy.The method works using an image segmentation technique and a classifier in MATLAB.The utilized classifier is a SupportVector Machine(SVM).DiscreteWavelet Transform(DWT)and Principal Component Analysis(PCA)are also involved.A dataset from the Kaggle website is used to test the developed approach.The obtained results reached nearly 99.2%of accuracy.The paper provides a confusion matrix of applying the proposed approach to testing images and a comparative evaluation between the developed method and some works in the literature.This evaluation shows that the presented system outperforms other approaches regarding the accuracy,precision,and recall.This research discovered that the developed method is extremely useful in detecting brain tumors,given the high accuracy,precision,and recall results.The proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial.

Skin Lesion Classification System Using Shearlets

The main cause of skin cancer is the ultraviolet radiation of the sun.It spreads quickly to other body parts.Thus,early diagnosis is required to decrease the mortality rate due to skin cancer.In this study,an automatic system for Skin Lesion Classification(SLC)using Non-Subsampled Shearlet Transform(NSST)based energy features and Support Vector Machine(SVM)classifier is proposed.Atfirst,the NSST is used for the decomposition of input skin lesion images with different directions like 2,4,8 and 16.From the NSST’s sub-bands,energy fea-tures are extracted and stored in the feature database for training.SVM classifier is used for the classification of skin lesion images.The dermoscopic skin images are obtained from PH^(2) database which comprises of 200 dermoscopic color images with melanocytic lesions.The performances of the SLC system are evaluated using the confusion matrix and Receiver Operating Characteristic(ROC)curves.The SLC system achieves 96%classification accuracy using NSST’s energy fea-tures obtained from 3^(rd) level with 8-directions.

A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

The recognition of pathological voice is considered a difficult task for speech analysis.Moreover,otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%.To enhance detection accuracy and reduce processing speed of dysphonia detection,a novel approach is proposed in this paper.We have leveraged Linear Discriminant Analysis(LDA)to train multiple Machine Learning(ML)models for dysphonia detection.Several ML models are utilized like Support Vector Machine(SVM),Logistic Regression,and K-nearest neighbor(K-NN)to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients(MFCC),Fundamental Frequency(F0),Shimmer(%),Jitter(%),and Harmonic to Noise Ratio(HNR).The experiments were performed using Saarbrucken Voice Data-base(SVD)and a privately collected dataset.The K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML models.According to the experimental results,our proposed approach has a 70%increase in processing speed over Principal Component Analysis(PCA)and performs remarkably well with a recognition accuracy of 95.24%on the SVD dataset surpassing the previous best accuracy of 82.37%.In the case of the private dataset,our proposed method achieved an accuracy rate of 93.37%.It can be an effective non-invasive method to detect dysphonia.

Rapid vision-based system for secondary copper content estimation

A vision-based color analysis system was developed for rapid estimation of copper content in the secondary copper smelting process. Firstly, cross section images of secondary copper samples were captured by the designed vision system. After the preprocessing and segmenting procedures, the images were selected according to their grayscale standard deviations of pixels and percentages of edge pixels in the luminance component. The selected images were then used to extract the information of the improved color vector angles, from which the copper content estimation model was developed based on the least squares support vector regression (LSSVR) method. For comparison, three additional LSSVR models, namely, only with sample selection, only with improved color vector angle, without sample selection or improved color vector angle, were developed. In addition, two exponential models, namely, with sample selection, without sample selection, were developed. Experimental results indicate that the proposed method is more effective for improving the copper content estimation accuracy, particularly when the sample size is small.

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

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.

AN IMPROVED ROTATION VECTOR ATTITUDE ALGORITHM FOR LASER STRAP-DOWN INERTIAL NAVIGATION SYSTEM

The laser gyro is most su it able for building the strap down inertial navigation system (SINS), and its acc uracy of attitude algorithm can enormously affect that of the laser SINS. This p aper develops three improved algorithmal expressions for strap down attitude ut ilizing the angular increment output by the laser gyro from the last two and cur rent updating periods according to the number of gyro samples, and analyses the algorithm error in the classical coning motion. Compared with the conventional algorithms, simulational results show that this improved algorithm has higher precision. A new way to improve the rotation vector algorithms is provided.

Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

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.

Coupling Analysis of Multiple Machine Learning Models for Human Activity Recognition

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.