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
The mechanical horizontal platform(MHP)system exhibits a rich chaotic behavior.The chaotic MHP system has applications in the earthquake and offshore industries.This article proposes a robust adaptive continuous contr...The mechanical horizontal platform(MHP)system exhibits a rich chaotic behavior.The chaotic MHP system has applications in the earthquake and offshore industries.This article proposes a robust adaptive continuous control(RACC)algorithm.It investigates the control and synchronization of chaos in the uncertain MHP system with time-delay in the presence of unknown state-dependent and time-dependent disturbances.The closed-loop system contains most of the nonlinear terms that enhance the complexity of the dynamical system;it improves the efficiency of the closed-loop.The proposed RACC approach(a)accomplishes faster convergence of the perturbed state variables(synchronization errors)to the desired steady-state,(b)eradicates the effect of unknown state-dependent and time-dependent disturbances,and(c)suppresses undesirable chattering in the feedback control inputs.This paper describes a detailed closed-loop stability analysis based on the Lyapunov-Krasovskii functional theory and Lyapunov stability technique.It provides parameter adaptation laws that confirm the convergence of the uncertain parameters to some constant values.The computer simulation results endorse the theoretical findings and provide a comparative performance.展开更多
Machine learning(ML)and data mining are used in various fields such as data analysis,prediction,image processing and especially in healthcare.Researchers in the past decade have focused on applying ML and data mining ...Machine learning(ML)and data mining are used in various fields such as data analysis,prediction,image processing and especially in healthcare.Researchers in the past decade have focused on applying ML and data mining to generate conclusions from historical data in order to improve healthcare systems by making predictions about the results.Using ML algorithms,researchers have developed applications for decision support,analyzed clinical aspects,extracted informative information from historical data,predicted the outcomes and categorized diseases which help physicians make better decisions.It is observed that there is a huge difference between women depending on the region and their social lives.Due to these differences,scholars have been encouraged to conduct studies at a local level in order to better understand those factors that affect maternal health and the expected child.In this study,the ensemble modeling technique is applied to classify birth outcomes based on either cesarean section(C-Section)or normal delivery.A voting ensemble model for the classification of a birth dataset was made by using a Random Forest(RF),Gradient Boosting Classifier,Extra Trees Classifier and Bagging Classifier as base learners.It is observed that the voting ensemble modal of proposed classifiers provides the best accuracy,i.e.,94.78%,as compared to the individual classifiers.ML algorithms are more accurate due to ensemble models,which reduce variance and classification errors.It is reported that when a suitable classification model has been developed for birth classification,decision support systems can be created to enable clinicians to gain in-depth insights into the patterns in the datasets.Developing such a system will not only allow health organizations to improve maternal health assessment processes,but also open doors for interdisciplinary research in two different fields in the region.展开更多
BACKGROUND Many studies have investigated the progression of nonalcoholic fatty liver disease(NAFLD)and its predisposing risk factors,but the conclusions from these studies have been conflicting.More challenging is th...BACKGROUND Many studies have investigated the progression of nonalcoholic fatty liver disease(NAFLD)and its predisposing risk factors,but the conclusions from these studies have been conflicting.More challenging is the fact that no effective treatment is currently available for NAFLD.AIM To determine the effects of proprotein convertase subtilisin/kexin type-9(PCSK9)inhibitors on fatty infiltration of the liver.METHODS This retrospective,chart review-based study was conducted on patients,18-yearold and above,who were currently on PCSK9 inhibitor drug therapy.Patients were excluded from the study according to missing pre-or post-treatment imaging or laboratory values,presence of cirrhosis or rhabdomyolysis,or development of acute liver injury during the PCSK9 inhibitor treatment period;the latter being due to false elevation of liver function markers,alanine aminotransferase(ALT)and aspartate aminotransferase(AST).Radiographic improvement was assessed by a single radiologist,who read both the pre-and post-treatment images to minimize reading bias.Fatty infiltration of the liver was also assessed by changes in ALT and AST,with pre-and post-treatment levels compared by paired t-test(alpha criterion:0.05).RESULTS Of the 29 patients included in the study,8 were male(27.6%)and 21 were female(72.4%).Essential hypertension was present in 25(86.2%)of the patients,diabetes mellitus in 18(62.1%)and obesity in 15(51.7%).In all,patients were on PCSK9 inhibitors for a mean duration of 23.69±11.18 mo until the most recent ALT and AST measures were obtained.Of the 11 patients who received the radiologic diagnosis of hepatic steatosis,8(72.73%)achieved complete radiologic resolution upon use of PCSK9 inhibitors(mean duration of 17.6 mo).On average,the ALT level(IU/L)decreased from 21.83±11.89 at pretreatment to 17.69±8.00 at posttreatment(2-tailed P=0.042)and AST level(IU/L)decreased from 22.48±9.00 pretreatment to 20.59±5.47 post-treatment(2-tailed P=0.201).CONCLUSION PCSK9 inhibitors can slow down or even completely resolve NAFLD.展开更多
With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and compute...With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation.However,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing data.It is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational complexity.Visual Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images.The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT.In this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved.By diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is mitigated.By fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing images.The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.展开更多
The modern paradigm of the Internet of Things(IoT)has led to a significant increase in demand for latency-sensitive applications in Fog-based cloud computing.However,such applications cannot meet strict quality of ser...The modern paradigm of the Internet of Things(IoT)has led to a significant increase in demand for latency-sensitive applications in Fog-based cloud computing.However,such applications cannot meet strict quality of service(QoS)requirements.The large-scale deployment of IoT requires more effective use of network infrastructure to ensure QoS when processing big data.Generally,cloud-centric IoT application deployment involves different modules running on terminal devices and cloud servers.Fog devices with different computing capabilities must process the data generated by the end device,so deploying latency-sensitive applications in a heterogeneous fog computing environment is a difficult task.In addition,when there is an inconsistent connection delay between the fog and the terminal device,the deployment of such applications becomes more complicated.In this article,we propose an algorithm that can effectively place application modules on network nodes while considering connection delay,processing power,and sensing data volume.Compared with traditional cloud computing deployment,we conducted simulations in iFogSim to confirm the effectiveness of the algorithm.The simulation results verify the effectiveness of the proposed algorithm in terms of end-to-end delay and network consumption.Therein,latency and execution time is insensitive to the number of sensors.展开更多
Laser-induced breakdown spectroscopy(LIBS) is a sensitive optical technique that is capable of rapid multi-elemental analysis. The development of this technique for elemental analysis of pharmaceutical products may ev...Laser-induced breakdown spectroscopy(LIBS) is a sensitive optical technique that is capable of rapid multi-elemental analysis. The development of this technique for elemental analysis of pharmaceutical products may eventually revolutionize the field of human health. Under normal circumstances, the elemental analysis of pharmaceutical products based on chemical methods is time-consuming and complicated. In this investigation, the principal aim is to develop an LIBS-based methodology for elemental analysis of pharmaceutical products. This LIBS technique was utilized for qualitative as well as quantitative analysis of the elements present in Ca-based tablets. All the elements present in the tablets were detected and their percentage compositions were verified in a single shot, using the proposed instrument. These elements(e.g., Ca, Mg, Fe, Zn, and others) were identified by the wavelengths of their spectral lines, which were verified using the NIST database. The approximate amount of each element was determined based on their observed peaks and the result was in exact agreement with the content specification. The determination of the composition of prescription drug for patients is highly important in numerous circumstances. For example, the exploitation of LIBS may facilitate elemental decomposition of medicines to determine the accuracy of the stated composition information. Moreover, the approach can provide element-specific, meaningful, and accurate information related to pharmaceutical products.展开更多
5G technology can greatly improve spectral efficiency(SE)and throughput of wireless communications.In this regard,multiple inputmultiple output(MIMO)technology has become the most influential technology using huge ant...5G technology can greatly improve spectral efficiency(SE)and throughput of wireless communications.In this regard,multiple inputmultiple output(MIMO)technology has become the most influential technology using huge antennas and user equipment(UE).However,the use of MIMO in 5G wireless technology will increase circuit power consumption and reduce energy efficiency(EE).In this regard,this article proposes an optimal solution for weighing SE and throughput tradeoff with energy efficiency.The research work is based on theWyner model of uplink(UL)and downlink(DL)transmission under the multi-cell model scenario.The SE-EE trade-off is carried out by optimizing the choice of antenna and UEs,while the approximation method based on the logarithmic function is used for optimization.In this paper,we analyzed the combination of UL and DL power consumption models and precoding schemes for all actual circuit power consumption models to optimize the trade-off between EE and throughput.The simulation results show that the SE-EE trade-off has been significantly improved by developing UL and DL transmission models with the approximation method based on logarithmic functions.It is also recognized that the throughput-EE trade-off can be improved by knowing the total actual power consumed by the entire network.展开更多
BACKGROUND Risk stratification tools exist for patients presenting with chest pain to the emergency room and have achieved the recommended negative predictive value(NPV)of 99%.However,due to low positive predictive va...BACKGROUND Risk stratification tools exist for patients presenting with chest pain to the emergency room and have achieved the recommended negative predictive value(NPV)of 99%.However,due to low positive predictive value(PPV),current stratification tools result in unwarranted investigations such as serial laboratory tests and cardiac stress tests(CSTs).AIM To create a machine learning model(MLM)for risk stratification of chest pain with a better PPV.METHODS This retrospective cohort study used de-identified hospital data from January 2016 until November 2021.Inclusion criteria were patients aged>21 years who presented to the ER,had at least two serum troponins measured,were subsequently admitted to the hospital,and had a CST within 4 d of presentation.Exclusion criteria were elevated troponin value(>0.05 ng/mL)and missing values for body mass index.The primary outcome was abnormal CST.Demographics,coronary artery disease(CAD)history,hypertension,hyperlipidemia,diabetes mellitus,chronic kidney disease,obesity,and smoking were evaluated as potential risk factors for abnormal CST.Patients were also categorized into a high-risk group(CAD history or more than two risk factors)and a low-risk group(all other patients)for comparison.Bivariate analysis was performed using a χ^(2) test or Fisher’s exact test.Age was compared by t test.Binomial regression(BR),random forest,and XGBoost MLMs were used for prediction.Bootstrapping was used for the internal validation of prediction models.BR was also used for inference.Alpha criterion was set at 0.05 for all statistical tests.R software was used for statistical analysis.RESULTS The final cohort of the study included 2328 patients,of which 245(10.52%)patients had abnormal CST.When adjusted for covariates in the BR model,male sex[risk ratio(RR)=1.52,95%confidence interval(CI):1.2-1.94,P<0.001],CAD history(RR=4.46,95%CI:3.08-6.72,P<0.001),and hyperlipidemia(RR=3.87,95%CI:2.12-8.12,P<0.001)remained statistically significant.Incidence of abnormal CST was 12.2%in the high-risk group and 2.3%in the low-risk group(RR=5.31,95%CI:2.75-10.24,P<0.001).The XGBoost model had the best PPV of 24.33%,with an NPV of 91.34%for abnormal CST.CONCLUSION The XGBoost MLM achieved a PPV of 24.33%for an abnormal CST,which is better than current stratification tools(13.00%-17.50%).This highlights the beneficial potential of MLMs in clinical decision-making.展开更多
BACKGROUND Liver transplant patients are at higher risk of infection due to immunosuppression.Whether liver transplant recipients are also more susceptible to severe acute respiratory syndrome coronavirus 2(SARS-CoV-2...BACKGROUND Liver transplant patients are at higher risk of infection due to immunosuppression.Whether liver transplant recipients are also more susceptible to severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)and will have worse outcomes than the general population if they develop coronavirus disease 2019(COVID-19)due to SARS-CoV-2 is a topic of ongoing studies,including ours.AIM To assess the clinical outcomes of COVID-19 in liver transplant recipients.METHODS This was a case-control study,with a database search performed(at the study site)from March 1,2020 through February 28,2021.Patients 18 years or older who tested positive for SARS-CoV-2 via polymerase chain reaction(PCR)were included in the study.Patients with infection other than pneumonia at the time of admission were excluded.After selection,patients who had been the recipient of liver transplant were considered cases and those without as controls.After being matched by age,sex,and obesity,two controls were randomly selected for each case.Death and hospitalization due to COVID-19 infection were the primary outcomes.Secondary outcomes were pertinent only to patients who were hospitalized,and they included duration of hospital stay,need for supplemental oxygen,presence of at least one type of end-organ damage,effects on liver enzymes,incidence of acute liver failure,effect on d-dimer levels,and incidence of venous thromboembolism(VTE).Chi-square or Fisher’s exact test was used to compare all primary and secondary outcomes with the exception of duration of hospital stay and d-dimer levels,which were compared using the Wilcoxon signed-rank test.Alpha criterion was set at 0.05.Logistic regression was performed for each primary outcome(as the dependent variable).Statistical analyses were performed using R software.RESULTS Of the 470 Liver transplant recipients who were tested for COVID-19 via the PCR test,39 patients tested positive(8.3%).There was no significant difference between cases and controls regarding death[odds ratio(OR):2.04,95%confidence interval(CI):0.14–29.17;P=0.60]and hospitalization rates(OR:1.38,95%CI:0.59–3.24;P=0.46).There also was no significant difference between cases and controls with respect to all secondary outcomes.Among all patients who had elevated liver enzymes,their levels were either normalized,improving,or remained stable at the time of discharge.No patient developed acute liver failure.Of the 31 hospitalized patients,27 received a prophylactic anticoagulation dose and no patient developed VTE in either group.Among cases who were hospitalized,immunosuppression was decreased in 5 patients and there was no change in immunosuppression among the remaining 7 patients.One patient died in each of these two subgroups.Logistic regression analysis was done,but all of the models had poor model predictions as well as insignificant predictors(independent variables).Therefore,they could not be used for either prediction or inference.CONCLUSION Clinical outcomes of COVID-19 in liver transplant recipients are not different than those without transplantation.COVID-19 should not impact timely health care access and immunosuppression continuation among these patients.展开更多
Carbon fiber reinforced high density polyethylene multi-layered laminated composite panels（HDPE/CF MLCP） with excellent in-plane properties along transverse direction have been formulated. Composite architectures wi...Carbon fiber reinforced high density polyethylene multi-layered laminated composite panels（HDPE/CF MLCP） with excellent in-plane properties along transverse direction have been formulated. Composite architectures with carbon fiber（CF） designed in 2D layout in conventional composites can alleviate their properties in thickness direction, but all attempts so far developed have achieved restrained success. Here,we have exposed an approach to the high strength composite challenge, without altering the 2D stack design on the basis of concept of fiber reinforced laminated composites that would provide enhanced mechanical and thermal properties along transverse direction. CF sheets allowed the buckling of adjoining plies in 2D MLCP. We fabricated 2D MLCP by stacking the alternative CF and HDPE layers under different loading conditions, which resulted in high strength composites. These plies of CF and HDPE served as unit cells for MLCP, with CF offering much-needed fracture toughness and hardness to these materials.For 2D HDPE/CF MLCP, we demonstrated noteworthy improvement in physical and chemical interaction between CF and HDPE, in-plane fracture strain, flexural strength（30.684 MPa）, bending modulus（7436.254 MPa）, thermal stability（40.94%）, and surface morphology, upon increasing the CF layers up to twenty, enabling these composites truly for high temperature and high strength applications.展开更多
A graph invariant is a number that can be easily and uniquely calculated through a graph.Recently,part of mathematical graph invariants has been portrayed and utilized for relationship examination.Nevertheless,no reli...A graph invariant is a number that can be easily and uniquely calculated through a graph.Recently,part of mathematical graph invariants has been portrayed and utilized for relationship examination.Nevertheless,no reliable appraisal has been embraced to pick,how much these invariants are associated with a network graph in interconnection networks of various fields of computer science,physics,and chemistry.In this paper,the study talks about sudoku networks will be networks of fractal nature having some applications in computer science like sudoku puzzle game,intelligent systems,Local area network(LAN)development and parallel processors interconnections,music composition creation,physics like power generation interconnections,Photovoltaic(PV)cells and chemistry,synthesis of chemical compounds.These networks are generally utilized in disorder,fractals,recursive groupings,and complex frameworks.Our outcomes are the normal speculations of currently accessible outcomes for specific classes of such kinds of networks of two unmistakable sorts with two invariants K-banhatti sombor(KBSO)invariants,Irregularity sombor(ISO)index,Contraharmonic-quadratic invariants(CQIs)and dharwad invariants with their reduced forms.The study solved the Sudoku network used in mentioned systems to improve the performance and find irregularities present in them.The calculated outcomes can be utilized for the modeling,scalability,introduction of new architectures of sudoku puzzle games,intelligent systems,PV cells,interconnection networks,chemical compounds,and extremely huge scope in very large-scale integrated circuits(VLSI)of processors.展开更多
In this paper,E-H mode transition in magnetic-pole-enhanced inductively coupled neon-argon mixture plasma is investigated in terms of fundamental plasma parameters as a function of argon fraction(0%-100%),operating pr...In this paper,E-H mode transition in magnetic-pole-enhanced inductively coupled neon-argon mixture plasma is investigated in terms of fundamental plasma parameters as a function of argon fraction(0%-100%),operating pressure(1 Pa,5 Pa,10 Pa and 50 Pa),and radio frequency(RF)power(5-100 W).An RF compensated Langmuir probe and optical emission spectroscopy are used for the diagnostics of the plasma under study.Owing to the lower ionization potential and higher collision cross-section of argon,when its fraction in the discharge is increased,the mode transition occurs at lower RF power;i.e.for 0%argon and1 Pa pressure,the threshold power of the E-H mode transition is 65 W,which reduces to 20 W when the argon fraction is increased.The electron density increases with the argon fraction at afixed pressure,whereas the temperature decreases with the argon fraction.The relaxation length of the low-energy electrons increases,and decreases for high-energy electrons with argon fraction,due to the Ramseur effect.However,the relaxation length of both groups of electrons decreases with pressure due to reduction in the mean free path.The electron energy probability function(EEPF)profiles are non-Maxwellian in E-mode,attributable to the nonlocal electron kinetics in this mode;however,they evolve to Maxwellian distribution when the discharge transforms to H-mode due to lower electron temperature and higher electron density in H-mode.The tail of the measured EEPFs is found to deplete in both E-and H-modes when the argon fraction in the discharge is increased,because argon has a much lower excitation potential(11.5 eV)than neon(16.6 eV).展开更多
In the present study,we aimed to investigate a protective role for resveratrol against the effects of immobilization stress on corpora lutea(CL)of mice in early pregnancy.A total of 45 early-pregnant mice were divided...In the present study,we aimed to investigate a protective role for resveratrol against the effects of immobilization stress on corpora lutea(CL)of mice in early pregnancy.A total of 45 early-pregnant mice were divided into no immobilization stress(NIS)group,immobilization stress(IS)group,and immobilization and resveratrol treatment(IS+RES)group(n=15).Mice were immobilized in plastic tubes(50 mL)for 3 h per day during day 1 to 7 of pregnancy.In the IS+RES group,5 mg kg-'d-1 of resveratrol was administered just prior to application of stress.We analyzed apoptotic activity in CL by Western botting analysis(WB),transmission electron microscopy(TEM),and immunohistochemistry(IHC).Serum progesterone levels were examined with radioimmunoassay(RIA).IHC results showed that the intensity of positive staining for Bax was increased,and for BcI-2 was decreased in CL after IS,while resveratrol treatment reversed the positive staining for Bax and Bcl-2.WB revealed that immobilization stress up-regulated the expression of Bax and caspase-9,and down-regulated Bcl-2 expression,while resveratrol treatment attenuated the effects of immobilization stress on the expression of Bax,Bcl-2 and caspase-9.According to our TEM results,apoptosis as defined by chromatin condensation was found in CL after immobilization stress,while resveratrol inhibited the apoptosis.We also demonstrated that immobilization stress decreased progesterone concentrations and ovarian expression of StAR,while resveratrol restored the concentrations of progesterone and expression of StAR back to normal.These results indicated that immobilization stress induced luteal regression while resveratrol inhibited luteal regression,suggesting that resveratrol plays a protective role on corpora lutea of mice during early pregnancy.展开更多
Desalination is considered a viable method to overcome the issue of water scarcity either from waste water or seawater. For this purpose, this study employed a facile approach to develop surface immobilized oxidized-M...Desalination is considered a viable method to overcome the issue of water scarcity either from waste water or seawater. For this purpose, this study employed a facile approach to develop surface immobilized oxidized-MWCNTs(o-MWCNTs) onto crosslinked polyvinyl alcohol(PVA) membrane. Firstly, modified polysulphone substrate was synthesized on to which crosslinked PVA layer was spread onto it. PVA layer act as active layer for surface immobilization of o-MWCNTs in varying concentration. The functional group analysis, morphology and roughness of membranes surface was conducted out using FTIR, SEM and AFM respectively. The results showed that modified membranes, immobilized o-MWCNTs enhanced the salt rejection(Na_(2)SO_(4)) upto 99.8%. After contacting with Escherichia coli and Staphylococcus aureus for 2.5 h the bacteria mortalities of the fabricated membrane could reach 96.9%. Furthermore, the antibiofouling tests showed that OP-MWCNTs(1-5) modified membranes have higher anti-biofouling property than the control membrane.展开更多
Farming is cultivating the soil,producing crops,and keeping livestock.The agricultural sector plays a crucial role in a country’s economic growth.This research proposes a two-stage machine learning framework for agri...Farming is cultivating the soil,producing crops,and keeping livestock.The agricultural sector plays a crucial role in a country’s economic growth.This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield.In the first stage,machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops.The recommended crops are based on various factors such as weather conditions,soil analysis,and the amount of fertilizers and pesticides required.In the second stage,a transfer learningbased model for plant seedlings,pests,and plant leaf disease datasets is used to detect weeds,pesticides,and diseases in the crop.The proposed model achieved an average accuracy of 95%,97%,and 98% in plant seedlings,pests,and plant leaf disease detection,respectively.The system can help farmers pinpoint the precise measures required at the right time to increase yields.展开更多
The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio(SNR).The proposed method utilizes the robust measures of location i.e.,the“Median...The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio(SNR).The proposed method utilizes the robust measures of location i.e.,the“Median”as well as the measures of variation i.e.,“Median absolute deviation(MAD)and Interquartile range(IQR)”in the SNR.By this way,two independent robust signal-to-noise ratios have been proposed.The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio(RSNR).The results obtained via the proposed method are compared with wellknown gene/feature selection methods on the basis of performance metric i.e.,classification error rate.A total of 5 gene expression datasets have been used in this study.Different subsets of informative genes are selected by the proposed and all the other methods included in the study,and their efficacy in terms of classification is investigated by using the classifier models such as support vector machine(SVM),Random forest(RF)and k-nearest neighbors(k-NN).The results of the analysis reveal that the proposed method(RSNR)produces minimum error rates than all the other competing feature selection methods in majority of the cases.For further assessment of the method,a detailed simulation study is also conducted.展开更多
An excessive use of non-linear devices in industry results in current harmonics that degrades the power quality with an unfavorable effect on power system performance.In this research,a novel control techniquebased Hy...An excessive use of non-linear devices in industry results in current harmonics that degrades the power quality with an unfavorable effect on power system performance.In this research,a novel control techniquebased Hybrid-Active Power-Filter(HAPF)is implemented for reactive power compensation and harmonic current component for balanced load by improving the Power-Factor(PF)and Total–Hormonic Distortion(THD)and the performance of a system.This work proposed a soft-computing technique based on Particle Swarm-Optimization(PSO)and Adaptive Fuzzy technique to avoid the phase delays caused by conventional control methods.Moreover,the control algorithms are implemented for an instantaneous reactive and active current(Id-Iq)and power theory(Pq0)in SIMULINK.To prevent the degradation effect of disturbances on the system’s performance,PS0-PI is applied in the inner loop which generate a required dc link-voltage.Additionally,a comparative analysis of both techniques has been presented to evaluate and validate the performance under balanced load conditions.The presented result concludes that the Adaptive Fuzzy PI controller performs better due to the non-linearity and robustness of the system.Therefore,the gains taken from a tuning of the PSO based PI controller optimized with Fuzzy Logic Controller(FLC)are optimal that will detect reactive power and harmonics much faster and accurately.The proposed hybrid technique minimizes distortion by selecting appropriate switching pulses for VSI(Voltage Source Inverter),and thus the simulation has been taken in SIMULINK/MATLAB.The proposed technique gives better tracking performance and robustness for reactive power compensation and harmonics mitigation.As a result of the comparison,it can be concluded that the PSO-basedAdaptive Fuzzy PI system produces accurate results with the lower THD and a power factor closer to unity than other techniques.展开更多
The effectiveness of the Business Intelligence(BI)system mainly depends on the quality of knowledge it produces.The decision-making process is hindered,and the user’s trust is lost,if the knowledge offered is undesir...The effectiveness of the Business Intelligence(BI)system mainly depends on the quality of knowledge it produces.The decision-making process is hindered,and the user’s trust is lost,if the knowledge offered is undesired or of poor quality.A Data Warehouse(DW)is a huge collection of data gathered from many sources and an important part of any BI solution to assist management in making better decisions.The Extract,Transform,and Load(ETL)process is the backbone of a DW system,and it is responsible for moving data from source systems into the DW system.The more mature the ETL process the more reliable the DW system.In this paper,we propose the ETL Maturity Model(EMM)that assists organizations in achieving a high-quality ETL system and thereby enhancing the quality of knowledge produced.The EMM is made up of five levels of maturity i.e.,Chaotic,Acceptable,Stable,Efficient and Reliable.Each level of maturity contains Key Process Areas(KPAs)that have been endorsed by industry experts and include all critical features of a good ETL system.Quality Objectives(QOs)are defined procedures that,when implemented,resulted in a high-quality ETL process.Each KPA has its own set of QOs,the execution of which meets the requirements of that KPA.Multiple brainstorming sessions with relevant industry experts helped to enhance the model.EMMwas deployed in two key projects utilizing multiple case studies to supplement the validation process and support our claim.This model can assist organizations in improving their current ETL process and transforming it into a more mature ETL system.This model can also provide high-quality information to assist users inmaking better decisions and gaining their trust.展开更多
The development of the Next-Generation Wireless Network(NGWN)is becoming a reality.To conduct specialized processes more,rapid network deployment has become essential.Methodologies like Network Function Virtualization...The development of the Next-Generation Wireless Network(NGWN)is becoming a reality.To conduct specialized processes more,rapid network deployment has become essential.Methodologies like Network Function Virtualization(NFV),Software-Defined Networks(SDN),and cloud computing will be crucial in addressing various challenges that 5G networks will face,particularly adaptability,scalability,and reliability.The motivation behind this work is to confirm the function of virtualization and the capabilities offered by various virtualization platforms,including hypervisors,clouds,and containers,which will serve as a guide to dealing with the stimulating environment of 5G.This is particularly crucial when implementing network operations at the edge of 5G networks,where limited resources and prompt user responses are mandatory.Experimental results prove that containers outperform hypervisor-based virtualized infrastructure and cloud platforms’latency and network throughput at the expense of higher virtualized processor use.In contrast to public clouds,where a set of rules is created to allow only the appropriate traffic,security is still a problem with containers.展开更多
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.
文摘The mechanical horizontal platform(MHP)system exhibits a rich chaotic behavior.The chaotic MHP system has applications in the earthquake and offshore industries.This article proposes a robust adaptive continuous control(RACC)algorithm.It investigates the control and synchronization of chaos in the uncertain MHP system with time-delay in the presence of unknown state-dependent and time-dependent disturbances.The closed-loop system contains most of the nonlinear terms that enhance the complexity of the dynamical system;it improves the efficiency of the closed-loop.The proposed RACC approach(a)accomplishes faster convergence of the perturbed state variables(synchronization errors)to the desired steady-state,(b)eradicates the effect of unknown state-dependent and time-dependent disturbances,and(c)suppresses undesirable chattering in the feedback control inputs.This paper describes a detailed closed-loop stability analysis based on the Lyapunov-Krasovskii functional theory and Lyapunov stability technique.It provides parameter adaptation laws that confirm the convergence of the uncertain parameters to some constant values.The computer simulation results endorse the theoretical findings and provide a comparative performance.
基金Natural Sciences and Engineering Research Council of Canada(NSERC)and New Brunswick Innovation Foundation(NBIF)for the financial support of the global project.These granting agencies did not contribute in the design of the study and collection,analysis,and interpretation of data。
文摘Machine learning(ML)and data mining are used in various fields such as data analysis,prediction,image processing and especially in healthcare.Researchers in the past decade have focused on applying ML and data mining to generate conclusions from historical data in order to improve healthcare systems by making predictions about the results.Using ML algorithms,researchers have developed applications for decision support,analyzed clinical aspects,extracted informative information from historical data,predicted the outcomes and categorized diseases which help physicians make better decisions.It is observed that there is a huge difference between women depending on the region and their social lives.Due to these differences,scholars have been encouraged to conduct studies at a local level in order to better understand those factors that affect maternal health and the expected child.In this study,the ensemble modeling technique is applied to classify birth outcomes based on either cesarean section(C-Section)or normal delivery.A voting ensemble model for the classification of a birth dataset was made by using a Random Forest(RF),Gradient Boosting Classifier,Extra Trees Classifier and Bagging Classifier as base learners.It is observed that the voting ensemble modal of proposed classifiers provides the best accuracy,i.e.,94.78%,as compared to the individual classifiers.ML algorithms are more accurate due to ensemble models,which reduce variance and classification errors.It is reported that when a suitable classification model has been developed for birth classification,decision support systems can be created to enable clinicians to gain in-depth insights into the patterns in the datasets.Developing such a system will not only allow health organizations to improve maternal health assessment processes,but also open doors for interdisciplinary research in two different fields in the region.
基金Data for this research project was obtained from Health System of the University of Kansas Medical Center,Kansas City,KS 66160,United States.The authors are grateful to the Department of Clinical Informatics at the University of Kansas Medical Center for their help in accessing the patient medical record database.Data extraction was conducted by the HERON automated data extraction tool.
文摘BACKGROUND Many studies have investigated the progression of nonalcoholic fatty liver disease(NAFLD)and its predisposing risk factors,but the conclusions from these studies have been conflicting.More challenging is the fact that no effective treatment is currently available for NAFLD.AIM To determine the effects of proprotein convertase subtilisin/kexin type-9(PCSK9)inhibitors on fatty infiltration of the liver.METHODS This retrospective,chart review-based study was conducted on patients,18-yearold and above,who were currently on PCSK9 inhibitor drug therapy.Patients were excluded from the study according to missing pre-or post-treatment imaging or laboratory values,presence of cirrhosis or rhabdomyolysis,or development of acute liver injury during the PCSK9 inhibitor treatment period;the latter being due to false elevation of liver function markers,alanine aminotransferase(ALT)and aspartate aminotransferase(AST).Radiographic improvement was assessed by a single radiologist,who read both the pre-and post-treatment images to minimize reading bias.Fatty infiltration of the liver was also assessed by changes in ALT and AST,with pre-and post-treatment levels compared by paired t-test(alpha criterion:0.05).RESULTS Of the 29 patients included in the study,8 were male(27.6%)and 21 were female(72.4%).Essential hypertension was present in 25(86.2%)of the patients,diabetes mellitus in 18(62.1%)and obesity in 15(51.7%).In all,patients were on PCSK9 inhibitors for a mean duration of 23.69±11.18 mo until the most recent ALT and AST measures were obtained.Of the 11 patients who received the radiologic diagnosis of hepatic steatosis,8(72.73%)achieved complete radiologic resolution upon use of PCSK9 inhibitors(mean duration of 17.6 mo).On average,the ALT level(IU/L)decreased from 21.83±11.89 at pretreatment to 17.69±8.00 at posttreatment(2-tailed P=0.042)and AST level(IU/L)decreased from 22.48±9.00 pretreatment to 20.59±5.47 post-treatment(2-tailed P=0.201).CONCLUSION PCSK9 inhibitors can slow down or even completely resolve NAFLD.
基金supported in part by the National Natural Science Foundation of China under Grants(62250410365,62071084)the Guangdong Basic and Applied Basic Research Foundation of China(2022A1515011542)the Guangzhou Science and technology program of China(202201010606).
文摘With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation.However,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing data.It is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational complexity.Visual Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images.The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT.In this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved.By diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is mitigated.By fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing images.The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.
基金This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2021-2016-0-00313)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘The modern paradigm of the Internet of Things(IoT)has led to a significant increase in demand for latency-sensitive applications in Fog-based cloud computing.However,such applications cannot meet strict quality of service(QoS)requirements.The large-scale deployment of IoT requires more effective use of network infrastructure to ensure QoS when processing big data.Generally,cloud-centric IoT application deployment involves different modules running on terminal devices and cloud servers.Fog devices with different computing capabilities must process the data generated by the end device,so deploying latency-sensitive applications in a heterogeneous fog computing environment is a difficult task.In addition,when there is an inconsistent connection delay between the fog and the terminal device,the deployment of such applications becomes more complicated.In this article,we propose an algorithm that can effectively place application modules on network nodes while considering connection delay,processing power,and sensing data volume.Compared with traditional cloud computing deployment,we conducted simulations in iFogSim to confirm the effectiveness of the algorithm.The simulation results verify the effectiveness of the proposed algorithm in terms of end-to-end delay and network consumption.Therein,latency and execution time is insensitive to the number of sensors.
文摘Laser-induced breakdown spectroscopy(LIBS) is a sensitive optical technique that is capable of rapid multi-elemental analysis. The development of this technique for elemental analysis of pharmaceutical products may eventually revolutionize the field of human health. Under normal circumstances, the elemental analysis of pharmaceutical products based on chemical methods is time-consuming and complicated. In this investigation, the principal aim is to develop an LIBS-based methodology for elemental analysis of pharmaceutical products. This LIBS technique was utilized for qualitative as well as quantitative analysis of the elements present in Ca-based tablets. All the elements present in the tablets were detected and their percentage compositions were verified in a single shot, using the proposed instrument. These elements(e.g., Ca, Mg, Fe, Zn, and others) were identified by the wavelengths of their spectral lines, which were verified using the NIST database. The approximate amount of each element was determined based on their observed peaks and the result was in exact agreement with the content specification. The determination of the composition of prescription drug for patients is highly important in numerous circumstances. For example, the exploitation of LIBS may facilitate elemental decomposition of medicines to determine the accuracy of the stated composition information. Moreover, the approach can provide element-specific, meaningful, and accurate information related to pharmaceutical products.
文摘5G technology can greatly improve spectral efficiency(SE)and throughput of wireless communications.In this regard,multiple inputmultiple output(MIMO)technology has become the most influential technology using huge antennas and user equipment(UE).However,the use of MIMO in 5G wireless technology will increase circuit power consumption and reduce energy efficiency(EE).In this regard,this article proposes an optimal solution for weighing SE and throughput tradeoff with energy efficiency.The research work is based on theWyner model of uplink(UL)and downlink(DL)transmission under the multi-cell model scenario.The SE-EE trade-off is carried out by optimizing the choice of antenna and UEs,while the approximation method based on the logarithmic function is used for optimization.In this paper,we analyzed the combination of UL and DL power consumption models and precoding schemes for all actual circuit power consumption models to optimize the trade-off between EE and throughput.The simulation results show that the SE-EE trade-off has been significantly improved by developing UL and DL transmission models with the approximation method based on logarithmic functions.It is also recognized that the throughput-EE trade-off can be improved by knowing the total actual power consumed by the entire network.
基金supported by the Clinical and Translational Science Award from the National Center for Advancing Translational Sciences,which has been awarded to the University of Kansas Clinical and Translational Science Institute.
文摘BACKGROUND Risk stratification tools exist for patients presenting with chest pain to the emergency room and have achieved the recommended negative predictive value(NPV)of 99%.However,due to low positive predictive value(PPV),current stratification tools result in unwarranted investigations such as serial laboratory tests and cardiac stress tests(CSTs).AIM To create a machine learning model(MLM)for risk stratification of chest pain with a better PPV.METHODS This retrospective cohort study used de-identified hospital data from January 2016 until November 2021.Inclusion criteria were patients aged>21 years who presented to the ER,had at least two serum troponins measured,were subsequently admitted to the hospital,and had a CST within 4 d of presentation.Exclusion criteria were elevated troponin value(>0.05 ng/mL)and missing values for body mass index.The primary outcome was abnormal CST.Demographics,coronary artery disease(CAD)history,hypertension,hyperlipidemia,diabetes mellitus,chronic kidney disease,obesity,and smoking were evaluated as potential risk factors for abnormal CST.Patients were also categorized into a high-risk group(CAD history or more than two risk factors)and a low-risk group(all other patients)for comparison.Bivariate analysis was performed using a χ^(2) test or Fisher’s exact test.Age was compared by t test.Binomial regression(BR),random forest,and XGBoost MLMs were used for prediction.Bootstrapping was used for the internal validation of prediction models.BR was also used for inference.Alpha criterion was set at 0.05 for all statistical tests.R software was used for statistical analysis.RESULTS The final cohort of the study included 2328 patients,of which 245(10.52%)patients had abnormal CST.When adjusted for covariates in the BR model,male sex[risk ratio(RR)=1.52,95%confidence interval(CI):1.2-1.94,P<0.001],CAD history(RR=4.46,95%CI:3.08-6.72,P<0.001),and hyperlipidemia(RR=3.87,95%CI:2.12-8.12,P<0.001)remained statistically significant.Incidence of abnormal CST was 12.2%in the high-risk group and 2.3%in the low-risk group(RR=5.31,95%CI:2.75-10.24,P<0.001).The XGBoost model had the best PPV of 24.33%,with an NPV of 91.34%for abnormal CST.CONCLUSION The XGBoost MLM achieved a PPV of 24.33%for an abnormal CST,which is better than current stratification tools(13.00%-17.50%).This highlights the beneficial potential of MLMs in clinical decision-making.
文摘BACKGROUND Liver transplant patients are at higher risk of infection due to immunosuppression.Whether liver transplant recipients are also more susceptible to severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)and will have worse outcomes than the general population if they develop coronavirus disease 2019(COVID-19)due to SARS-CoV-2 is a topic of ongoing studies,including ours.AIM To assess the clinical outcomes of COVID-19 in liver transplant recipients.METHODS This was a case-control study,with a database search performed(at the study site)from March 1,2020 through February 28,2021.Patients 18 years or older who tested positive for SARS-CoV-2 via polymerase chain reaction(PCR)were included in the study.Patients with infection other than pneumonia at the time of admission were excluded.After selection,patients who had been the recipient of liver transplant were considered cases and those without as controls.After being matched by age,sex,and obesity,two controls were randomly selected for each case.Death and hospitalization due to COVID-19 infection were the primary outcomes.Secondary outcomes were pertinent only to patients who were hospitalized,and they included duration of hospital stay,need for supplemental oxygen,presence of at least one type of end-organ damage,effects on liver enzymes,incidence of acute liver failure,effect on d-dimer levels,and incidence of venous thromboembolism(VTE).Chi-square or Fisher’s exact test was used to compare all primary and secondary outcomes with the exception of duration of hospital stay and d-dimer levels,which were compared using the Wilcoxon signed-rank test.Alpha criterion was set at 0.05.Logistic regression was performed for each primary outcome(as the dependent variable).Statistical analyses were performed using R software.RESULTS Of the 470 Liver transplant recipients who were tested for COVID-19 via the PCR test,39 patients tested positive(8.3%).There was no significant difference between cases and controls regarding death[odds ratio(OR):2.04,95%confidence interval(CI):0.14–29.17;P=0.60]and hospitalization rates(OR:1.38,95%CI:0.59–3.24;P=0.46).There also was no significant difference between cases and controls with respect to all secondary outcomes.Among all patients who had elevated liver enzymes,their levels were either normalized,improving,or remained stable at the time of discharge.No patient developed acute liver failure.Of the 31 hospitalized patients,27 received a prophylactic anticoagulation dose and no patient developed VTE in either group.Among cases who were hospitalized,immunosuppression was decreased in 5 patients and there was no change in immunosuppression among the remaining 7 patients.One patient died in each of these two subgroups.Logistic regression analysis was done,but all of the models had poor model predictions as well as insignificant predictors(independent variables).Therefore,they could not be used for either prediction or inference.CONCLUSION Clinical outcomes of COVID-19 in liver transplant recipients are not different than those without transplantation.COVID-19 should not impact timely health care access and immunosuppression continuation among these patients.
文摘Carbon fiber reinforced high density polyethylene multi-layered laminated composite panels（HDPE/CF MLCP） with excellent in-plane properties along transverse direction have been formulated. Composite architectures with carbon fiber（CF） designed in 2D layout in conventional composites can alleviate their properties in thickness direction, but all attempts so far developed have achieved restrained success. Here,we have exposed an approach to the high strength composite challenge, without altering the 2D stack design on the basis of concept of fiber reinforced laminated composites that would provide enhanced mechanical and thermal properties along transverse direction. CF sheets allowed the buckling of adjoining plies in 2D MLCP. We fabricated 2D MLCP by stacking the alternative CF and HDPE layers under different loading conditions, which resulted in high strength composites. These plies of CF and HDPE served as unit cells for MLCP, with CF offering much-needed fracture toughness and hardness to these materials.For 2D HDPE/CF MLCP, we demonstrated noteworthy improvement in physical and chemical interaction between CF and HDPE, in-plane fracture strain, flexural strength（30.684 MPa）, bending modulus（7436.254 MPa）, thermal stability（40.94%）, and surface morphology, upon increasing the CF layers up to twenty, enabling these composites truly for high temperature and high strength applications.
基金King Saud University through Researchers Supporting Project number(RSP2022R426),King Saud University,Riyadh,Saudi Arabia.
文摘A graph invariant is a number that can be easily and uniquely calculated through a graph.Recently,part of mathematical graph invariants has been portrayed and utilized for relationship examination.Nevertheless,no reliable appraisal has been embraced to pick,how much these invariants are associated with a network graph in interconnection networks of various fields of computer science,physics,and chemistry.In this paper,the study talks about sudoku networks will be networks of fractal nature having some applications in computer science like sudoku puzzle game,intelligent systems,Local area network(LAN)development and parallel processors interconnections,music composition creation,physics like power generation interconnections,Photovoltaic(PV)cells and chemistry,synthesis of chemical compounds.These networks are generally utilized in disorder,fractals,recursive groupings,and complex frameworks.Our outcomes are the normal speculations of currently accessible outcomes for specific classes of such kinds of networks of two unmistakable sorts with two invariants K-banhatti sombor(KBSO)invariants,Irregularity sombor(ISO)index,Contraharmonic-quadratic invariants(CQIs)and dharwad invariants with their reduced forms.The study solved the Sudoku network used in mentioned systems to improve the performance and find irregularities present in them.The calculated outcomes can be utilized for the modeling,scalability,introduction of new architectures of sudoku puzzle games,intelligent systems,PV cells,interconnection networks,chemical compounds,and extremely huge scope in very large-scale integrated circuits(VLSI)of processors.
基金partially supported by Quaid-i-Azam University URF for the year 2019-2020Higher Education Commission(HEC)P.No.820 for Plasma Physics Gomal University(D I Khan)。
文摘In this paper,E-H mode transition in magnetic-pole-enhanced inductively coupled neon-argon mixture plasma is investigated in terms of fundamental plasma parameters as a function of argon fraction(0%-100%),operating pressure(1 Pa,5 Pa,10 Pa and 50 Pa),and radio frequency(RF)power(5-100 W).An RF compensated Langmuir probe and optical emission spectroscopy are used for the diagnostics of the plasma under study.Owing to the lower ionization potential and higher collision cross-section of argon,when its fraction in the discharge is increased,the mode transition occurs at lower RF power;i.e.for 0%argon and1 Pa pressure,the threshold power of the E-H mode transition is 65 W,which reduces to 20 W when the argon fraction is increased.The electron density increases with the argon fraction at afixed pressure,whereas the temperature decreases with the argon fraction.The relaxation length of the low-energy electrons increases,and decreases for high-energy electrons with argon fraction,due to the Ramseur effect.However,the relaxation length of both groups of electrons decreases with pressure due to reduction in the mean free path.The electron energy probability function(EEPF)profiles are non-Maxwellian in E-mode,attributable to the nonlocal electron kinetics in this mode;however,they evolve to Maxwellian distribution when the discharge transforms to H-mode due to lower electron temperature and higher electron density in H-mode.The tail of the measured EEPFs is found to deplete in both E-and H-modes when the argon fraction in the discharge is increased,because argon has a much lower excitation potential(11.5 eV)than neon(16.6 eV).
基金The authors wish to thank Prof.Emeritus Reinhold J.Hutz,PhD of the Department of Biological Sciences,University of Wisconsin-Milwaukee,USA,for his editing and helpful adviceThis work was supported by the National Natural Science Foundation of China(31501956 and 31572403).
文摘In the present study,we aimed to investigate a protective role for resveratrol against the effects of immobilization stress on corpora lutea(CL)of mice in early pregnancy.A total of 45 early-pregnant mice were divided into no immobilization stress(NIS)group,immobilization stress(IS)group,and immobilization and resveratrol treatment(IS+RES)group(n=15).Mice were immobilized in plastic tubes(50 mL)for 3 h per day during day 1 to 7 of pregnancy.In the IS+RES group,5 mg kg-'d-1 of resveratrol was administered just prior to application of stress.We analyzed apoptotic activity in CL by Western botting analysis(WB),transmission electron microscopy(TEM),and immunohistochemistry(IHC).Serum progesterone levels were examined with radioimmunoassay(RIA).IHC results showed that the intensity of positive staining for Bax was increased,and for BcI-2 was decreased in CL after IS,while resveratrol treatment reversed the positive staining for Bax and Bcl-2.WB revealed that immobilization stress up-regulated the expression of Bax and caspase-9,and down-regulated Bcl-2 expression,while resveratrol treatment attenuated the effects of immobilization stress on the expression of Bax,Bcl-2 and caspase-9.According to our TEM results,apoptosis as defined by chromatin condensation was found in CL after immobilization stress,while resveratrol inhibited the apoptosis.We also demonstrated that immobilization stress decreased progesterone concentrations and ovarian expression of StAR,while resveratrol restored the concentrations of progesterone and expression of StAR back to normal.These results indicated that immobilization stress induced luteal regression while resveratrol inhibited luteal regression,suggesting that resveratrol plays a protective role on corpora lutea of mice during early pregnancy.
文摘Desalination is considered a viable method to overcome the issue of water scarcity either from waste water or seawater. For this purpose, this study employed a facile approach to develop surface immobilized oxidized-MWCNTs(o-MWCNTs) onto crosslinked polyvinyl alcohol(PVA) membrane. Firstly, modified polysulphone substrate was synthesized on to which crosslinked PVA layer was spread onto it. PVA layer act as active layer for surface immobilization of o-MWCNTs in varying concentration. The functional group analysis, morphology and roughness of membranes surface was conducted out using FTIR, SEM and AFM respectively. The results showed that modified membranes, immobilized o-MWCNTs enhanced the salt rejection(Na_(2)SO_(4)) upto 99.8%. After contacting with Escherichia coli and Staphylococcus aureus for 2.5 h the bacteria mortalities of the fabricated membrane could reach 96.9%. Furthermore, the antibiofouling tests showed that OP-MWCNTs(1-5) modified membranes have higher anti-biofouling property than the control membrane.
基金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Farming is cultivating the soil,producing crops,and keeping livestock.The agricultural sector plays a crucial role in a country’s economic growth.This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield.In the first stage,machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops.The recommended crops are based on various factors such as weather conditions,soil analysis,and the amount of fertilizers and pesticides required.In the second stage,a transfer learningbased model for plant seedlings,pests,and plant leaf disease datasets is used to detect weeds,pesticides,and diseases in the crop.The proposed model achieved an average accuracy of 95%,97%,and 98% in plant seedlings,pests,and plant leaf disease detection,respectively.The system can help farmers pinpoint the precise measures required at the right time to increase yields.
基金King Saud University for funding this work through Researchers Supporting Project Number(RSP2022R426),King Saud University,Riyadh,Saudi Arabia.
文摘The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio(SNR).The proposed method utilizes the robust measures of location i.e.,the“Median”as well as the measures of variation i.e.,“Median absolute deviation(MAD)and Interquartile range(IQR)”in the SNR.By this way,two independent robust signal-to-noise ratios have been proposed.The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio(RSNR).The results obtained via the proposed method are compared with wellknown gene/feature selection methods on the basis of performance metric i.e.,classification error rate.A total of 5 gene expression datasets have been used in this study.Different subsets of informative genes are selected by the proposed and all the other methods included in the study,and their efficacy in terms of classification is investigated by using the classifier models such as support vector machine(SVM),Random forest(RF)and k-nearest neighbors(k-NN).The results of the analysis reveal that the proposed method(RSNR)produces minimum error rates than all the other competing feature selection methods in majority of the cases.For further assessment of the method,a detailed simulation study is also conducted.
基金This work was supported by the King Saud University,Riyadh,Saudi Arabia,through Researchers Supporting Project number RSP-2021/184.
文摘An excessive use of non-linear devices in industry results in current harmonics that degrades the power quality with an unfavorable effect on power system performance.In this research,a novel control techniquebased Hybrid-Active Power-Filter(HAPF)is implemented for reactive power compensation and harmonic current component for balanced load by improving the Power-Factor(PF)and Total–Hormonic Distortion(THD)and the performance of a system.This work proposed a soft-computing technique based on Particle Swarm-Optimization(PSO)and Adaptive Fuzzy technique to avoid the phase delays caused by conventional control methods.Moreover,the control algorithms are implemented for an instantaneous reactive and active current(Id-Iq)and power theory(Pq0)in SIMULINK.To prevent the degradation effect of disturbances on the system’s performance,PS0-PI is applied in the inner loop which generate a required dc link-voltage.Additionally,a comparative analysis of both techniques has been presented to evaluate and validate the performance under balanced load conditions.The presented result concludes that the Adaptive Fuzzy PI controller performs better due to the non-linearity and robustness of the system.Therefore,the gains taken from a tuning of the PSO based PI controller optimized with Fuzzy Logic Controller(FLC)are optimal that will detect reactive power and harmonics much faster and accurately.The proposed hybrid technique minimizes distortion by selecting appropriate switching pulses for VSI(Voltage Source Inverter),and thus the simulation has been taken in SIMULINK/MATLAB.The proposed technique gives better tracking performance and robustness for reactive power compensation and harmonics mitigation.As a result of the comparison,it can be concluded that the PSO-basedAdaptive Fuzzy PI system produces accurate results with the lower THD and a power factor closer to unity than other techniques.
基金King Saud University for funding this work through Researchers Supporting Project Number(RSP-2021/387),King Saud University,Riyadh,Saudi Arabia.
文摘The effectiveness of the Business Intelligence(BI)system mainly depends on the quality of knowledge it produces.The decision-making process is hindered,and the user’s trust is lost,if the knowledge offered is undesired or of poor quality.A Data Warehouse(DW)is a huge collection of data gathered from many sources and an important part of any BI solution to assist management in making better decisions.The Extract,Transform,and Load(ETL)process is the backbone of a DW system,and it is responsible for moving data from source systems into the DW system.The more mature the ETL process the more reliable the DW system.In this paper,we propose the ETL Maturity Model(EMM)that assists organizations in achieving a high-quality ETL system and thereby enhancing the quality of knowledge produced.The EMM is made up of five levels of maturity i.e.,Chaotic,Acceptable,Stable,Efficient and Reliable.Each level of maturity contains Key Process Areas(KPAs)that have been endorsed by industry experts and include all critical features of a good ETL system.Quality Objectives(QOs)are defined procedures that,when implemented,resulted in a high-quality ETL process.Each KPA has its own set of QOs,the execution of which meets the requirements of that KPA.Multiple brainstorming sessions with relevant industry experts helped to enhance the model.EMMwas deployed in two key projects utilizing multiple case studies to supplement the validation process and support our claim.This model can assist organizations in improving their current ETL process and transforming it into a more mature ETL system.This model can also provide high-quality information to assist users inmaking better decisions and gaining their trust.
基金supported by Future University Researchers Supporting Project Number FUESP-2020/48 at Future University in Egypt,New Cairo 11845,Egypt.
文摘The development of the Next-Generation Wireless Network(NGWN)is becoming a reality.To conduct specialized processes more,rapid network deployment has become essential.Methodologies like Network Function Virtualization(NFV),Software-Defined Networks(SDN),and cloud computing will be crucial in addressing various challenges that 5G networks will face,particularly adaptability,scalability,and reliability.The motivation behind this work is to confirm the function of virtualization and the capabilities offered by various virtualization platforms,including hypervisors,clouds,and containers,which will serve as a guide to dealing with the stimulating environment of 5G.This is particularly crucial when implementing network operations at the edge of 5G networks,where limited resources and prompt user responses are mandatory.Experimental results prove that containers outperform hypervisor-based virtualized infrastructure and cloud platforms’latency and network throughput at the expense of higher virtualized processor use.In contrast to public clouds,where a set of rules is created to allow only the appropriate traffic,security is still a problem with containers.
