An Efficient Modelling of Oversampling with Optimal Deep Learning Enabled Anomaly Detection in Streaming Data

Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.

MA-Res U-Net:Design of Soybean Navigation System with Improved U-Net Model

Traditional machine vision algorithms have difficulty handling the interference of light and shadow changes,broken rows,and weeds in the complex growth circumstances of soybean fields,which leads to erroneous navigation route segmentation.There are additional shortcomings in the feature extractFion capabilities of the conventional U-Net network.Our suggestion is to utilize an improved U-Net-based method to tackle these difficulties.First,we use ResNet’s powerful feature extraction capabilities to replace the original U-Net encoder.To enhance the concentration on characteristics unique to soybeans,we integrate a multi-scale high-performance attention mechanism.Furthermore,to do multi-scale feature extraction and capture a wider variety of contextual information,we employ atrous spatial pyramid pooling.The segmented image generated by our upgraded U-Net model is then analyzed using the CenterNet method to extract key spots.The RANSAC algorithm then uses these important spots to delineate the soybean seedling belt line.Finally,the navigation line is determined using the angle tangency theory.The experimental findings illustrate the superiority of our method.Our improved model significantly outperforms the original U-Net regarding mean Pixel Accuracy(mPA)and mean Intersection over Union(mIOU)indices,showing a more accurate segmentation of soybean routes.Furthermore,our soybean route navigation system’s outstanding accuracy is demonstrated by the deviation angle,which is only 3°between the actual deviation and the navigation line.This technology makes a substantial contribution to the sustainable growth of agriculture and shows potential for real-world applications.

Four-function model analysis of nonsuicidal self-injury-related posts on Sina Weibo

To the editor:Non-suicidal self-injury(NSSI)is defined as direct,repetitive self-injury to bodily tissues without suicidal intent. The estimated prevalence of NsSI among adolescents is 17.2%worldwide with a comparable rate observed in China. As a behavioural addiction, NSSI poses a significant suicide risk,and is emerging as a major mental health problem among adolescents.To unravel this puzzle,the four-function model(FFM)distinguishes between interpersonal and intrapersonal functions,as well as positive and negative reinforcement functions,proving relatively comprehensive among various theoretical models.

Predictive Modeling of Sedentary Behavior Patterns in Adults Using Stacked LSTM

A new health concern in recent periods has seen the evolution of uncertain sedentary behavior.Remaining sedentary for extended durations is regarded as a notable hazard across various adult age brackets,especially the excessive dependence on automobiles for transportation.Throughout the active period,monitoring seating habits has been made easier by sensors.Nevertheless,there exists a disagreement among professionals regarding the most suitable quantifiable criteria for encompassing the comprehensive data on sedentary behavior throughout the day.Owing to variations in measurement methodologies,data analysis approaches,and the lack of essential outcome indicators such as the total sedentary duration,the assessment of sedentary patterns in numerous research investigations was considered unfeasible.The research suggested fleeting granularity distinguish occurrences of regular human activities.Sophisticated units(essential cells) acquire multivariate transitory information.Frequent Behavior Patterns(FBPs) can be identified with a estimation of timeframe using our proposed scalable algorithms that employ collected widespread multivariate data(fleeting granularity).The research outcome,supported by rigorous analyses on two validated datasets,mark a significant progression.In the final stages of the study,a stacked Long Short-Term Memory(LSTM) model was utilized to replicate and forecast repetitive sedentary behavior patterns,leveraging data from the preceding six-hour window blocks of sedentary activity.The model effectively replicated state traits,previous action sequences,and duration,attaining an impressive 99% accuracy level as assessed through RMSE,MSE,MAPE,and r-correlation metrics.

A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Nowadays,the COVID-19 virus disease is spreading rampantly.There are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited count.To diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are needed.The enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting COVID-19.The most common symptoms of COVID-19 are fever,dry cough and sore throat.These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier.Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death rate.Here,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and classification.This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models.At last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of class.With the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is estimated.The experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.

Hierarchical Data Aggregation with Data Offloading Scheme for Fog Enabled IoT Environment

Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things(IoT)services.After the emergence of IoT-based services,the industry of internet-based devices has grown.The number of these devices has raised from millions to billions,and it is expected to increase further in the near future.Thus,additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in real-time without affecting the user experience.Conventional data aggregation models for Fog enabled IoT environ-ments possess high computational complexity and communication cost.There-fore,in order to resolve the issues and improve the lifetime of the network,this study develops an effective hierarchical data aggregation with chaotic barnacles mating optimizer(HDAG-CBMO)technique.The HDAG-CBMO technique derives afitness function from many relational matrices,like residual energy,average distance to neighbors,and centroid degree of target area.Besides,a chaotic theory based population initialization technique is derived for the optimal initial position of barnacles.Moreover,a learning based data offloading method has been developed for reducing the response time to IoT user requests.A wide range of simulation analyses demonstrated that the HDAG-CBMO technique has resulted in balanced energy utilization and prolonged lifetime of the Fog assisted IoT networks.

Mathematical Study of HD Micropolar Fluid Flow with Radiation and Dissipative Impacts over a Permeable Stretching Sheet:Slip Effects Phenomena

The purpose of this research is to investigate the influence that slip boundary conditions have on the rate of heat and mass transfer by examining the behavior of micropolar MHD flow across a porous stretching sheet.In addition to this,the impacts of thermal radiation and viscous dissipation are taken into account.With the use of various computing strategies,numerical results have been produced.Similarity transformation was utilized in order to convert the partial differential equations(PDEs)that regulated energy,rotational momentum,concentration,and momentum into ordinary differential equations(ODEs).As compared to earlier published research,MATLAB inbuilt solver solution shows an extremely good correlation in exceptional instances.In exceptional instances,the present MATLAB inbuilt solver solution has a very excellent connection with the findings of the previously published investigations.A variety of flow field factors impact the Nusselt number,the wall couple shear stress,the friction factor,Sherwood numbers the dimensionless distributions discussed in detail.When the Eckert number rises,the temperature rises,and the Schmidt number falls,the concentration falls.Velocity increases with increases in the material factor but drops with increases in the magnetic parameter and the surface condition factor.

Effect on Optical and Antibacterial Activity of SnO2 and CuO Blended SnO2 Nanoparticles

Nanocrystalline SnO2 and CuO doped with SnO2 were prepared by the co-precipitation method and characterized for different physiochemical properties and microbiological activity. The composition and morphological formation were characterized by XRD, HRTEM, Raman, FTIR, and UV-vis spectroscopy. The Powder X-ray analysis reveals that Sn4+ ions have substituted the Cu2+ ions without changing the monoclinic structure of SnO2 but the average particle size of the SnO2 and CuO doped SnO2 samples from 11 and 5 nm respectively. However, it exhibits an inhibiting strong bacterial growth against tested bacterial strains.

Modeling the System for Hybrid Renewable Energy Using Highly Efficient Converters and Generator

High-efficient isolated DC/DC converters with a high-efficiency synchronous reluctance generator(SRG)are the ultimate solutions in DC microgrid systems.The design and modeling of isolated DC/DC converters with the performance of SRG are carried out.On the generator side,reactive and active powers are used as pulse width modulation(PWM)control variables.Further,the flux estimator is used.Three-phase PWM rectifier is used by applying space vector modulation(SVM)with a constant switching frequency for direct power control.Further,the paper also includes the experimental validation of the results.The paper also proposes that highly efficient power converters and synchronous reluctance generators are required to achieve high performance for hybrid renewable energy systems applications.

SHT-based public auditing protocol with error tolerance in FDL-empowered IoVs

With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep learning.Federated Deep Learning(FDL)is a novel distributed machine learning technology and is able to address the challenges like data security,privacy risks,and huge communication overheads from big raw data sets.However,FDL can only guarantee data security and privacy among multiple clients during data training.If the data sets stored locally in clients are corrupted,including being tampered with and lost,the training results of the FDL in intelligent IoVs must be negatively affected.In this paper,we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered IoVs.Specifically,the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance,including efficient corrupted data locating and recovery.In addition,a novel data structure,Skip Hash Table(SHT)is designed to optimize data dynamics.Finally,we illustrate the security of the scheme with the Computational Diffie-Hellman(CDH)assumption on bilinear groups.Sufficient theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.

Finite Difference Approach on Magnetohydrodynamic Stratified Fluid Flow Past Vertically Accelerated Plate in Porous Media with Viscous Dissipation

This study intends to evaluate the influence of temperature stratification on an unsteady fluid flow past an accelerated vertical plate in the existence of viscous dissipation.It is assumed that the medium under study is a grey,non-scattered fluid that both fascinates and transmits radiation.The leading equations are discretized using the finite differencemethod(FDM).UsingMATLABsoftware,the impacts of flowfactors on flowfields are revealed with particular examples in graphs and a table.In this regard,FDM results show that the velocity and temperature gradients increase with an increase of Eckert number.Furthermore,tables of the data indicate the influence of flow-contributing factors on the skin friction coefficients,and Nusselt numbers.When comparing constant and variable flow regimes,the constant flow regime has greater values for the nondimensional skin friction coefficient.This research is both innovative and fascinating since it has the potential to expand our understanding of fluid dynamics and to improve many different sectors.

Mechanical Properties of V-,Nb-,and Ti-bearing As-cast Microalloyed Steels

The influence of microalloying additions on the mechanical properties of a low-carbon cast steel containing combinations of V, Nb, and Ti in the as-cast condition was evaluated. Tensile and hardness test results indicated that good combinations of strength and ductility could be achieved by V and Nb additions. While the yield strength and UTS （ultimate tensile strength） increased up to the range of 378-435 MPa and 579- 590 MPa, respectively in the microalloyed heats, their total elongation ranged from 18% to 23%. The presence of Ti, however, led to some reduction in the strength. Microstructural studies including scanning electron microscopy （SEM） and optical microscopy revealed that coarse TiN particles were responsible for this behavior. The Charpy impact values of all compositions indicated that microalloying additions significantly decreased the impact energy and led to the dominance of cleavage facets on the fracture surfaces. It seems that the increase in the hardness of coarse ferrite grains due to the precipitation hardening is the main reason for brittle fracture.

Modeling and optimization of low-grade Mn bearing ore leaching using response surface methodology and central composite rotatable design

The application of leaching process to extracting Mn from a low-grade manganese ore was investigated using a software based design of experiments. Four main parameters, i.e. sulfuric acid concentration, oxalic acid concentration, time and temperature were considered in a central composite response surface design. The recoveries of Mn and Fe were selected as response of design. The optimum conditions under which the Mn and Fe recoveries were the highest and the time and temperature were the lowest were determined using statistical analysis and analysis of variance （ANOVA）. The results showed that Mn and Fe recoveries were 93.44% and 15.72% under the optimum condition, respectively. Also, sulfuric acid concentration was the most effective parameter affecting the process. The amounts of sulfuric and oxalic acid were obtained to be 7% and 42.50 g/L in optimum condition and the best time and temperature were 65 min and 63 ℃.

Effect of plating parameters on microstructure and tribological properties of Co-BN (hexagonal) nano composite coatings

Effects of current density, duty cycle and frequency on microstructure and particles content of electrodeposited Co-BN （hexagonal） nano composite coatings were analyzed by SEM, FESEM, EDS, AFM and XRD techniques. The microhardness, tribological behavior and wear mechanism were also investigated. Generally, as the current density and frequency increased, the particles content and microhardness of the coatings increased firstly and then decreased. Moreover, by reducing duty cycle, more particles were incorporated and higher microhardness was obtained. The best tribological behavior was achieved under the conditions duty cycle of 10%, frequency of 50 Hz and current density of 100 mA/cm2.

Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization algorithms.The optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the image.To test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation.The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate.In contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time values.Further,it was observed thatthe segmented image gives greater detail when the threshold level increases.Moreover,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.

Dendritic cells derived from pluripotent stem cells:Potential of large scale production

Human pluripotent stem cells(hPSCs), including human embryonic stem cells and human induced pluripotent stem cells, are promising sources for hematopoietic cells due to their unlimited growth capacity and the pluripotency. Dendritic cells(DCs), the unique immune cells in the hematopoietic system, can be loaded with tumor specific antigen and used as vaccine for cancer immunotherapy. While autologous DCs from peripheral blood are limited in cell number, hPSC-derived DCs provide a novel alternative cell source which has the potential for large scale production. This review summarizes recent advances in differentiating hPSCs to DCs through the intermediate stage of hematopoietic stem cells. Step-wise growth factor induction has been used to derive DCs from hPSCs either in suspension cultureof embryoid bodies(EBs) or in co-culture with stromal cells. To fulfill the clinical potential of the DCs derived from hPSCs, the bioprocess needs to be scaled up to produce a large number of cells economically under tight quality control. This requires the development of novel bioreactor systems combining guided EB-based differentiation with engineered culture environment. Hence, recent progress in using bioreactors for hPSC lineage-specific differentiation is reviewed. In particular, the potential scale up strategies for the multistage DC differentiation and the effect of shear stress on hPSC differentiation in bioreactors are discussed in detail.

Occurrence mechanism of silicate and aluminosilicate minerals in Sarcheshmeh copper flotation concentrate

The Sarcheshmeh copper flotation circuit is producing 5× 10^4 t copper concentrate per month with an averaging grade of 28% Cu in rougher, cleaner and recleaner stages. In recent years, with the increase in the open pit depth, the content of aluminosilicate minerals increased in plant feed and subsequently in flotation concentrate. It can motivate some problems, such as unwanted consumption of reagents, decreasing of the copper concentrate grade, increasing of Al2O3 and SiO2 in the copper concentrate, and needing a higher temperature in the smelting process. The evaluation of the composite samples related to the most critical working period of the plant shows that quartz, illite, biotite, chlorite, orthoclase, albeit, muscovite, and kaolinite are the major Al2O3 and SiO2 beating minerals that accompany chalcopyrite, chalcocite, and covellite minerals in the plant feed. The severe alteration to clay minerals was a general rule in all thin sections that were prepared from the plant feed. Sieve analysis of the flotation concentrate shows that Al2O3 and SiO2 bearing minerals in the flotation concentrate can be decreased by promoting the size reduction from 53 to 38 μm. Interlocking of the Al2O3 and SiO2 bearing minerals with chalcopyrite and chalcocite is the occurrence mechanism of silicate and aluminosilicate minerals in the flotation concentrate. The dispersed form of interlocking is predominant.

Tight Focusing Properties of Radially Polarized Gaussian Beams with Pair of Vortices

The tight focusing properties of a radially polarized Gaussian beam with a nested pair of vortices having a radial wave front distribution are investigated theoretically by the vector diffraction theory. The results show that the optical intensity in the focal region can be altered considerably by changing the location of the vortices nested in a radially polarized Gaussian beam. It is noted that focal evolution from one annular focal pattern to a highly confined focal spot in the transverse direction is observed corresponding to the change in the location of the optical vortices in the input plane. It is also observed that the generated focal hole or spot lead to a focal shift along the optical axis remarkably under proper radial phase modulation. Hence the proposed system may be applied to construct tunable optical traps for both high and low refractive index particles.

Antioxidant and antipyretic properties of methanolic extract of Amaranthus spinosus leaves

Objective:Methanolic extract of Amaranthus spinosus（A.spinosus） leaves was screened for antioxidant and antipyretic activities.Methods:Antioxidant activity was measured by l,l-diphenyl-2-picryl-hydrazile（DPPH） free radical scavenging,superoxide anion radical scavenging,hydroxyl free radical scavenging,nitric oxide radical scavenging,2,2 -azinobis-3- ethylbenzothiazole-6-sulfonic acid（ABTS） radical scavenging assays and total phenolic content was also determined.Antipyretic activity of methanolic extract of A.spinosus was measured by yeast induced pyrexia method at concentration of 200 and 400 mg/kg using paracetamol as standard drug.Results:Methanolic extract of A.spinosus showed potent antioxidant activity.The IC50 value was（87.50±3.52）μg/mL,（98.80±1.40）μg/mL,（106.25±0.20）μg/mL,（88.70±0.62）μg/mL and（147.50±2.61）μg/mL for DPPH,superoxide,hydroxyl,nitric oxide and ABTS radical scavenging activities.Methanolic extract of A spinosus showed significant（P【0.01） antipyretic activity.

A validated stability-indicating LC method for the separation of enantiomer and potential impurities of Linezolid using polar organic mode

Although a number of methods are available for evaluating Linezolid and its possible impurities, a common method for separation if its potential impurities, degradants and enantiomer in a single method with good efficiency remain unavailable. With the objective of developing an advanced method with shorter runtimes, a simple, precise, accurate stability-indicating LC method was developed for the determination of purity of Linezolid drug substance and drug products in bulk samples and pharmaceutical dosage forms in the presence of its impurities and degradation products. This method is capable of separating all the related substances of Linezolid along with the chiral impurity. This method can also be used for the estimation of assay of Linezolid in drug substance as well as in drug product. The method was developed using Chiralpak IA (250 mm 4.6 mm, 5 mm) column. A mixture of acetonitrile, ethanol, n-butyl amine and trifluoro acetic acid in 96:4:0.10:0.16 (v/v/v/v) ratio was used as a mobile phase. The eluted compounds were monitored at 254 nm. Linezolid was subjected to the stress conditions of oxidative, acid, base, hydrolytic, thermal and photolytic degradation. The degradation products were well resolved from main peak and its impurities, proving the stability-indicating power of the method. The developed method was validated as per International Conference on Harmonization (ICH) guidelines with respect to specificity, limit of detection, limit of quantification, precision, linearity, accuracy, robustness and system suitability.