In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health infor...In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.展开更多
The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software w...The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.展开更多
Statistical literacy is crucial for cultivating well-rounded thinkers.The integration of evidence-based strategies in teaching and learning is pivotal for enhancing students’statistical literacy.This research specifi...Statistical literacy is crucial for cultivating well-rounded thinkers.The integration of evidence-based strategies in teaching and learning is pivotal for enhancing students’statistical literacy.This research specifically focuses on the utilization of Share and Model Concepts and Nurturing Metacognition as evidence-based strategies aimed at improving the statistical literacy of learners.The study employed a quasi-experimental design,specifically the nonequivalent control group,wherein students answered pre-test and post-test instruments and researcher-made questionnaires.The study included 50 first-year Bachelor in Secondary Education majors in Mathematics and Science for the academic year 2023-2024.The results of the study revealed a significant difference in the scores of student respondents,indicating that the use of evidence-based strategies helped students enhance their statistical literacy.This signifies a noteworthy increase in their performance,ranging from very low to very high proficiency in understanding statistical concepts,insights into the application of statistical concepts,numeracy,graph skills,interpretation capabilities,and visualization and communication skills.Furthermore,the study showed a significant difference in the post-test scores’performance of the two groups in understanding statistical concepts and visualization and communication skills.However,no significant difference was found in the post-test scores of the two groups concerning insights into the application of statistical concepts,numeracy and graph skills,and interpretation capabilities.Additionally,students acknowledged that the implementation of evidence-based strategies significantly contributed to the improvement of their statistical literacy.展开更多
Electrical impedance tomography (EIT) aims to reconstruct the conductivity distribution using the boundary measured voltage potential. Traditional regularization based method would suffer from error propagation due to...Electrical impedance tomography (EIT) aims to reconstruct the conductivity distribution using the boundary measured voltage potential. Traditional regularization based method would suffer from error propagation due to the iteration process. The statistical inverse problem method uses statistical inference to estimate unknown parameters. In this article, we develop a nonlinear weighted anisotropic total variation (NWATV) prior density function based on the recently proposed NWATV regularization method. We calculate the corresponding posterior density function, i.e., the solution of the EIT inverse problem in the statistical sense, via a modified Markov chain Monte Carlo (MCMC) sampling. We do numerical experiment to validate the proposed approach.展开更多
A multi-objective linear programming problem is made from fuzzy linear programming problem. It is due the fact that it is used fuzzy programming method during the solution. The Multi objective linear programming probl...A multi-objective linear programming problem is made from fuzzy linear programming problem. It is due the fact that it is used fuzzy programming method during the solution. The Multi objective linear programming problem can be converted into the single objective function by various methods as Chandra Sen’s method, weighted sum method, ranking function method, statistical averaging method. In this paper, Chandra Sen’s method and statistical averaging method both are used here for making single objective function from multi-objective function. Two multi-objective programming problems are solved to verify the result. One is numerical example and the other is real life example. Then the problems are solved by ordinary simplex method and fuzzy programming method. It can be seen that fuzzy programming method gives better optimal values than the ordinary simplex method.展开更多
Background:Survival from birth to slaughter is an important economic trait in commercial pig productions.Increasing survival can improve both economic efficiency and animal welfare.The aim of this study is to explore ...Background:Survival from birth to slaughter is an important economic trait in commercial pig productions.Increasing survival can improve both economic efficiency and animal welfare.The aim of this study is to explore the impact of genotyping strategies and statistical models on the accuracy of genomic prediction for survival in pigs during the total growing period from birth to slaughter.Results:We simulated pig populations with different direct and maternal heritabilities and used a linear mixed model,a logit model,and a probit model to predict genomic breeding values of pig survival based on data of individual survival records with binary outcomes(0,1).The results show that in the case of only alive animals having genotype data,unbiased genomic predictions can be achieved when using variances estimated from pedigreebased model.Models using genomic information achieved up to 59.2%higher accuracy of estimated breeding value compared to pedigree-based model,dependent on genotyping scenarios.The scenario of genotyping all individuals,both dead and alive individuals,obtained the highest accuracy.When an equal number of individuals(80%)were genotyped,random sample of individuals with genotypes achieved higher accuracy than only alive individuals with genotypes.The linear model,logit model and probit model achieved similar accuracy.Conclusions:Our conclusion is that genomic prediction of pig survival is feasible in the situation that only alive pigs have genotypes,but genomic information of dead individuals can increase accuracy of genomic prediction by 2.06%to 6.04%.展开更多
We present a statistical investigation of the degree of influence that assumptions made in relation to the mechanical parameters of a pylon have on its ground-induced vibrations.The study is set up by using as a key k...We present a statistical investigation of the degree of influence that assumptions made in relation to the mechanical parameters of a pylon have on its ground-induced vibrations.The study is set up by using as a key kinematic variable the displacement at the top of a reference,a stand-alone pylon with a uniform cross-section and fixity at its base.Next,statistics are produced using a dimensionless displacement ratio defined between the‘parental’and the‘subsidiary’cases,the latter defined for the pylon(a)resting on compliant soil,(b)having an attached top mass,and(c)being non-uniform with height.Furthermore,two materials are examined,namely,steel and reinforced concrete(R/C).More specifically,this displacement ratio is independent of the excitation and plays the role of a transfer function between the base and the top of the pylon.Both horizontal and vertical motions are considered,and the equations of motion are solved in the frequency domain.The ensuing statistical analysis is conducted for the following parameter combinations:(a)pylon founded on soft,intermediate,and stiff soil;(b)low,intermediate,and high-mass ratios of the attached mass to the pylon′s mass;(c)a constant and quadratic degree of pylon tapering with height.Spearman correlation coefficients are calculated for all the above combinations to arrive at statistical results that establish validity bounds and quantify the degree of influence of each assumption on the pylon′s response.展开更多
Quantum computers promise to solve finite-temperature properties of quantum many-body systems,which is generally challenging for classical computers due to high computational complexities.Here,we report experimental p...Quantum computers promise to solve finite-temperature properties of quantum many-body systems,which is generally challenging for classical computers due to high computational complexities.Here,we report experimental preparations of Gibbs states and excited states of Heisenberg X X and X X Z models by using a 5-qubit programmable superconducting processor.In the experiments,we apply a hybrid quantum–classical algorithm to generate finite temperature states with classical probability models and variational quantum circuits.We reveal that the Hamiltonians can be fully diagonalized with optimized quantum circuits,which enable us to prepare excited states at arbitrary energy density.We demonstrate that the approach has a self-verifying feature and can estimate fundamental thermal observables with a small statistical error.Based on numerical results,we further show that the time complexity of our approach scales polynomially in the number of qubits,revealing its potential in solving large-scale problems.展开更多
Convolutional neural networks(CNNs) have been widely studied and found to obtain favorable results in statistical downscaling to derive high-resolution climate variables from large-scale coarse general circulation mod...Convolutional neural networks(CNNs) have been widely studied and found to obtain favorable results in statistical downscaling to derive high-resolution climate variables from large-scale coarse general circulation models(GCMs).However, there is a lack of research exploring the predictor selection for CNN modeling. This paper presents an effective and efficient greedy elimination algorithm to address this problem. The algorithm has three main steps: predictor importance attribution, predictor removal, and CNN retraining, which are performed sequentially and iteratively. The importance of individual predictors is measured by a gradient-based importance metric computed by a CNN backpropagation technique, which was initially proposed for CNN interpretation. The algorithm is tested on the CNN-based statistical downscaling of monthly precipitation with 20 candidate predictors and compared with a correlation analysisbased approach. Linear models are implemented as benchmarks. The experiments illustrate that the predictor selection solution can reduce the number of input predictors by more than half, improve the accuracy of both linear and CNN models,and outperform the correlation analysis method. Although the RMSE(root-mean-square error) is reduced by only 0.8%,only 9 out of 20 predictors are used to build the CNN, and the FLOPs(Floating Point Operations) decrease by 20.4%. The results imply that the algorithm can find subset predictors that correlate more to the monthly precipitation of the target area and seasons in a nonlinear way. It is worth mentioning that the algorithm is compatible with other CNN models with stacked variables as input and has the potential for nonlinear correlation predictor selection.展开更多
Phase-matching quantum key distribution is a promising scheme for remote quantum key distribution,breaking through the traditional linear key-rate bound.In practical applications,finite data size can cause significant...Phase-matching quantum key distribution is a promising scheme for remote quantum key distribution,breaking through the traditional linear key-rate bound.In practical applications,finite data size can cause significant system performance to deteriorate when data size is below 1010.In this work,an improved statistical fluctuation analysis method is applied for the first time to two decoy-states phase-matching quantum key distribution,offering a new insight and potential solutions for improving the key generation rate and the maximum transmission distance while maintaining security.Moreover,we also compare the influence of the proposed improved statistical fluctuation analysis method on system performance with those of the Gaussian approximation and Chernoff-Hoeffding boundary methods on system performance.The simulation results show that the proposed scheme significantly improves the key generation rate and maximum transmission distance in comparison with the Chernoff-Hoeffding approach,and approach the results obtained when the Gaussian approximation is employed.At the same time,the proposed scheme retains the same security level as the Chernoff-Hoeffding method,and is even more secure than the Gaussian approximation.展开更多
A method for packing irregular particles with a prescribed volume fraction is proposed.Furthermore,the generated granular material adheres to the prescribed statistical distribution and satisfies the desired complex s...A method for packing irregular particles with a prescribed volume fraction is proposed.Furthermore,the generated granular material adheres to the prescribed statistical distribution and satisfies the desired complex spatial arrangement.First,the irregular geometries of the realistic particles were obtained from the original particle images.Second,the Minkowski sum was used to check the overlap between irregular particles and place an irregular particle in contact with other particles.Third,the optimised advance front method(OAFM)generated irregular particle packing with the prescribed statistical dis-tribution and volume fraction based on the Minkowski sum.Moreover,the signed distance function was introduced to pack the particles in accordance with the desired spatial arrangement.Finally,seven biaxial tests were performed using the UDEC software,which demonstrated the accuracy and potential usefulness of the proposed method.It can model granular material efficiently and reflect the meso-structural characteristics of complex granular materials.This method has a wide range of applications where discrete modelling of granular media is necessary.展开更多
In this paper,we consider a reconfigurable intelligent surface(RIS)-assisted multiple-input multiple-output(MIMO)secure communication system,where only legitimate user's(Bob's)statistical channel state informa...In this paper,we consider a reconfigurable intelligent surface(RIS)-assisted multiple-input multiple-output(MIMO)secure communication system,where only legitimate user's(Bob's)statistical channel state information(CSI)can be obtained at the transmitter(Alice),while eavesdropper's(Eve's)CSI is unknown.Firstly,the analytical expression of the achievable ergodic rate at Bob is obtained.Then,by exploiting Bob's statistical CSI,we jointly design the transmit covariance matrix at Alice and the phase shift matrix at the RIS to minimize the transmit power of the information signal under the quality-of-service(QoS)constraint of Bob.Finally,we propose an artificial noise(AN)-aided method without Eve's CSI to enhance the security of this system and use the residual power to design the transmit covariance for AN.Simulation results verify the convergence of the proposed method,and also show that there exists a trade-off between the secrecy rate and QoS of Bob.展开更多
Predicting potential risks associated with the fatigue of key structural components is crucial in engineering design.However,fatigue often involves entangled complexities of material microstructures and service condit...Predicting potential risks associated with the fatigue of key structural components is crucial in engineering design.However,fatigue often involves entangled complexities of material microstructures and service conditions,making diagnosis and prognosis of fatigue damage challenging.We report a statistical learning framework to predict the growth of fatigue cracks and the life-to-failure of the components under loading conditions with uncertainties.Digital libraries of fatigue crack patterns and the remaining life are constructed by high-fidelity physical simulations.Dimensionality reduction and neural network architectures are then used to learn the history dependence and nonlinearity of fatigue crack growth.Path-slicing and re-weighting techniques are introduced to handle the statistical noises and rare events.The predicted fatigue crack patterns are self-updated and self-corrected by the evolving crack patterns.The end-to-end approach is validated by representative examples with fatigue cracks in plates,which showcase the digital-twin scenario in real-time structural health monitoring and fatigue life prediction for maintenance management decision-making.展开更多
Alfvén ion cyclotron waves(ACWs)and kinetic Alfvén waves(KAWs)are found to exist at<0.3 au observed by Parker Solar Probe in Alfvénic slow solar winds.To examine the statistical properties of the bac...Alfvén ion cyclotron waves(ACWs)and kinetic Alfvén waves(KAWs)are found to exist at<0.3 au observed by Parker Solar Probe in Alfvénic slow solar winds.To examine the statistical properties of the background parameters for ACWs and KAWs and related wave disturbances,both wave events observed by Parker Solar Probe are selected and analyzed.The results show that there are obvious differences in the background and disturbance parameters between ACWs and KAWs.ACW events have a relatively higher occurrence rate but with a total duration slightly shorter than KAW events.The median background magnetic field magnitude and the related background solar wind speed of KAW events are larger than those of ACWs.The distributions of the relative disturbances of the proton velocity,proton temperature,the proton number density,andβcover wider ranges for ACW events than for KAW events.The results may be important for the understanding of the nature and characteristics of Alfvénic slow solar wind fluctuations at ion scales near the Sun,and provide the information of the background field and plasma parameters and the wave disturbances of ACWs and KAWs for further relevant theoretical modeling or numerical simulations.展开更多
Background:Patients with colon cancer who receive chemotherapy usually experience various gastrointestinal adverse reactions,including nausea,vomiting,and diarrhea,which make it challenging for them to adhere to treat...Background:Patients with colon cancer who receive chemotherapy usually experience various gastrointestinal adverse reactions,including nausea,vomiting,and diarrhea,which make it challenging for them to adhere to treatment.As an effective traditional Chinese medicine,the Jianpi Bushen formula has been widely used to alleviate the side effects of chemotherapy.Objective:To evaluate the efficacy and safety of Jianpi Bushen formulae for patients who undergo chemotherapy.This statistical analysis plan(SAP)is intended to enhance the transparency and research quality of our randomized controlled trial.Methods:Our study is a multicenter,double-blind,randomized controlled clinical trial.This trial aimed to compare the completion rate of chemotherapy in colon cancer patients who are using and not using Jianpi Bushen formula.To attenuate possible selection bias in the final report,we declared the overall trial design,outcome measures,subgroup analyses,and safety measures.Also,we described the data management and statistical analysis methods in detail.Conclusion:The SAP provides more detailed information than the trial protocol for data management and statistical analysis methods.Further post-hoc analyses can be performed by referring to the SAP,and possible selection bias can be attenuated.展开更多
The value of a statistical life(VSL)is a crucial tool for monetizing health impacts.To explore the VSL in China,this study examines people’s willingness to pay(WTP)to reduce death risk from air pollution in six repre...The value of a statistical life(VSL)is a crucial tool for monetizing health impacts.To explore the VSL in China,this study examines people’s willingness to pay(WTP)to reduce death risk from air pollution in six representative cities in China based on face-to-face contingent valuation interviews(n=3936)from March 7,2019 to September 30,2019.The results reveal that the WTP varied from CNY 455 to 763 in 2019(USD 66-111),corresponding to a VSL range of CNY 3.79-6.36 million(USD 549395-921940).The VSL in China in 2019 is estimated to be CNY 4.76 million(USD 689659).The statistics indicate that monthly expenditure levels,environmental concerns,risk attitudes,and assumed market acceptance,which have seldom been dis‐cussed in previous studies,significantly impact WTP and VSL.These findings will serve as a reference for ana‐lyzing mortality risk reduction benefits in future research and for policymaking.展开更多
Recently,gender equality and women’s entrepreneurship have gained considerable attention in global economic development.Prior to the design of any policy interventions to increase women’s entrepreneurship,it is sign...Recently,gender equality and women’s entrepreneurship have gained considerable attention in global economic development.Prior to the design of any policy interventions to increase women’s entrepreneurship,it is significant to comprehend the factors motivating women to become entrepreneurs.The non-understanding of the factors can result in the endurance of low living stan-dards and the design of expensive and ineffectual policies.But female involve-ment in entrepreneurship becomes higher in developing economies compared to developed economies.Women Entrepreneurship Index(WEI)plays a vital role in determining the factors that enable theflourishment of high potential female entrepreneurs which enhances economic welfare and contributes to the economic and social fabric of society.Therefore,it is needed to design an automated and accurate WEI prediction model to improve women’s entrepreneurship.In this view,this article develops an automated statistical analysis enabled WEI predic-tive(ASA-WEIP)model.The proposed ASA-WEIP technique aims to effectually determine the WEI.The proposed ASA-WEIP technique encompasses a series of sub-processes such as pre-processing,WEI prediction,and parameter optimiza-tion.For the prediction of WEI,the ASA-WEIP technique makes use of the Deep Belief Network(DBN)model,and the parameter optimization process takes place using Squirrel Search Algorithm(SSA).The performance validation of the ASA-WEIP technique was executed using the benchmark dataset from the Kaggle repo-sitory.The experimental outcomes stated the better outcomes of the ASA-WEIP technique over the other existing techniques.展开更多
Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so on.Statistical data mining(SDM)is an interdisciplinary dom...Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so on.Statistical data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data.It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves.Thus,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)model.In the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation complexity.For rainfall prediction.Convolution neural network with long short-term memory(CNN-LSTM)technique is exploited.At last,this study involves the pelican optimization algorithm(POA)as a hyperparameter optimizer.The experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive class.The comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.展开更多
The frequent occurrence of dry and hot(DH)days in South China in summer has a negative impact on social development and human health.This study explored the variation characteristics of DH days and the possible reason...The frequent occurrence of dry and hot(DH)days in South China in summer has a negative impact on social development and human health.This study explored the variation characteristics of DH days and the possible reasons for this knotty problem.The findings revealed a notable increase in the number of DH days across most stations,indicating a significant upward trend.Additionally,DH events were observed to occur frequently.The number of DH days increased during 1970-1990,decreased from 1991 to 1997,and stayed stable after 1997.The key climate factors affecting the interannual variability of the number of DH days were the Indian Ocean Basin warming(IOBW)in spring and the East Asian Summer Monsoon(EASM).Compared with the negative phase of IOBW,in the positive phase of IOBW,500 hPa and 850 hPa geopotential height enhanced,the West Pacific subtropical high strengthened and extended abnormally to the west,more solar radiation reached the surface,surface outgoing longwave radiation increased,and there was an anomalous anticyclone in eastern South China.The atmospheric circulation characteristics of the positive and negative phases of ESAM were opposite to those of IOBW,and the abnormal circulation of the positive(negative)phases of ESAM was unfavorable(favorable)for the increase in the number of DH days.A long-term prediction model for the number of summer DH days was established using multiple linear regression,incorporating the key climate factors.The correlation coefficient between the observed and predicted number of DH days was 0.65,and the root-mean-square error was 2.8.In addition,independent forecasts for 2019 showed a deviation of just 1 day.The results of the independent recovery test confirmed the stability of the model,providing evidence that climatic factors did have an impact on DH days in South China.展开更多
The study of land surface temperature(LST)is of great significance for ecosystem monitoring and ecological environmental protection in the Qinling Mountains of China.In view of the contradicting spatial and temporal r...The study of land surface temperature(LST)is of great significance for ecosystem monitoring and ecological environmental protection in the Qinling Mountains of China.In view of the contradicting spatial and temporal resolutions in extracting LST from satellite remote sensing(RS)data,the areas with complex landforms of the Eastern Qinling Mountains were selected as the research targets to establish the correlation between the normalized difference vegetation index(NDVI)and LST.Detailed information on the surface features and temporal changes in the land surface was provided by Sentinel-2 and Sentinel-3,respectively.Based on the statistically downscaling method,the spatial scale could be decreased from 1000 m to 10 m,and LST with a Sentinel-3 temporal resolution and a 10 m spatial resolution could be retrieved.Comparing the 1 km resolution Sentinel-3 LST with the downscaling results,the 10 m LST downscaling data could accurately reflect the spatial distribution of the thermal characteristics of the original LST image.Moreover,the surface temperature data with a 10 m high spatial resolution had clear texture and obvious geomorphic features that could depict the detailed information of the ground features.The results showed that the average error was 5 K on April 16,2019 and 2.6 K on July 15,2019.The smaller error values indicated the higher vegetation coverage of summer downscaling result with the highest level on July 15.展开更多
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R194)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.
文摘The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.
文摘Statistical literacy is crucial for cultivating well-rounded thinkers.The integration of evidence-based strategies in teaching and learning is pivotal for enhancing students’statistical literacy.This research specifically focuses on the utilization of Share and Model Concepts and Nurturing Metacognition as evidence-based strategies aimed at improving the statistical literacy of learners.The study employed a quasi-experimental design,specifically the nonequivalent control group,wherein students answered pre-test and post-test instruments and researcher-made questionnaires.The study included 50 first-year Bachelor in Secondary Education majors in Mathematics and Science for the academic year 2023-2024.The results of the study revealed a significant difference in the scores of student respondents,indicating that the use of evidence-based strategies helped students enhance their statistical literacy.This signifies a noteworthy increase in their performance,ranging from very low to very high proficiency in understanding statistical concepts,insights into the application of statistical concepts,numeracy,graph skills,interpretation capabilities,and visualization and communication skills.Furthermore,the study showed a significant difference in the post-test scores’performance of the two groups in understanding statistical concepts and visualization and communication skills.However,no significant difference was found in the post-test scores of the two groups concerning insights into the application of statistical concepts,numeracy and graph skills,and interpretation capabilities.Additionally,students acknowledged that the implementation of evidence-based strategies significantly contributed to the improvement of their statistical literacy.
文摘Electrical impedance tomography (EIT) aims to reconstruct the conductivity distribution using the boundary measured voltage potential. Traditional regularization based method would suffer from error propagation due to the iteration process. The statistical inverse problem method uses statistical inference to estimate unknown parameters. In this article, we develop a nonlinear weighted anisotropic total variation (NWATV) prior density function based on the recently proposed NWATV regularization method. We calculate the corresponding posterior density function, i.e., the solution of the EIT inverse problem in the statistical sense, via a modified Markov chain Monte Carlo (MCMC) sampling. We do numerical experiment to validate the proposed approach.
文摘A multi-objective linear programming problem is made from fuzzy linear programming problem. It is due the fact that it is used fuzzy programming method during the solution. The Multi objective linear programming problem can be converted into the single objective function by various methods as Chandra Sen’s method, weighted sum method, ranking function method, statistical averaging method. In this paper, Chandra Sen’s method and statistical averaging method both are used here for making single objective function from multi-objective function. Two multi-objective programming problems are solved to verify the result. One is numerical example and the other is real life example. Then the problems are solved by ordinary simplex method and fuzzy programming method. It can be seen that fuzzy programming method gives better optimal values than the ordinary simplex method.
基金funded by the"Genetic improvement of pig survival"project from Danish Pig Levy Foundation (Aarhus,Denmark)The China Scholarship Council (CSC)for providing scholarship to the first author。
文摘Background:Survival from birth to slaughter is an important economic trait in commercial pig productions.Increasing survival can improve both economic efficiency and animal welfare.The aim of this study is to explore the impact of genotyping strategies and statistical models on the accuracy of genomic prediction for survival in pigs during the total growing period from birth to slaughter.Results:We simulated pig populations with different direct and maternal heritabilities and used a linear mixed model,a logit model,and a probit model to predict genomic breeding values of pig survival based on data of individual survival records with binary outcomes(0,1).The results show that in the case of only alive animals having genotype data,unbiased genomic predictions can be achieved when using variances estimated from pedigreebased model.Models using genomic information achieved up to 59.2%higher accuracy of estimated breeding value compared to pedigree-based model,dependent on genotyping scenarios.The scenario of genotyping all individuals,both dead and alive individuals,obtained the highest accuracy.When an equal number of individuals(80%)were genotyped,random sample of individuals with genotypes achieved higher accuracy than only alive individuals with genotypes.The linear model,logit model and probit model achieved similar accuracy.Conclusions:Our conclusion is that genomic prediction of pig survival is feasible in the situation that only alive pigs have genotypes,but genomic information of dead individuals can increase accuracy of genomic prediction by 2.06%to 6.04%.
基金support of the German Research Foundation (DFG) through Grant SM 281/20-1the Hellenic Foundation for Research and Innovation (HFRI) under the 3rd Call for PhD fellowships (Fellowship Number: 6522)
文摘We present a statistical investigation of the degree of influence that assumptions made in relation to the mechanical parameters of a pylon have on its ground-induced vibrations.The study is set up by using as a key kinematic variable the displacement at the top of a reference,a stand-alone pylon with a uniform cross-section and fixity at its base.Next,statistics are produced using a dimensionless displacement ratio defined between the‘parental’and the‘subsidiary’cases,the latter defined for the pylon(a)resting on compliant soil,(b)having an attached top mass,and(c)being non-uniform with height.Furthermore,two materials are examined,namely,steel and reinforced concrete(R/C).More specifically,this displacement ratio is independent of the excitation and plays the role of a transfer function between the base and the top of the pylon.Both horizontal and vertical motions are considered,and the equations of motion are solved in the frequency domain.The ensuing statistical analysis is conducted for the following parameter combinations:(a)pylon founded on soft,intermediate,and stiff soil;(b)low,intermediate,and high-mass ratios of the attached mass to the pylon′s mass;(c)a constant and quadratic degree of pylon tapering with height.Spearman correlation coefficients are calculated for all the above combinations to arrive at statistical results that establish validity bounds and quantify the degree of influence of each assumption on the pylon′s response.
基金Project supported by the State Key Development Program for Basic Research of China(Grant No.2017YFA0304300)the National Natural Science Foundation of China(Grant Nos.11934018,11747601,and 11975294)+4 种基金Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB28000000)Scientific Instrument Developing Project of Chinese Academy of Sciences(Grant No.YJKYYQ20200041)Beijing Natural Science Foundation(Grant No.Z200009)the Key-Area Research and Development Program of Guangdong Province,China(Grant No.2020B0303030001)Chinese Academy of Sciences(Grant No.QYZDB-SSW-SYS032)。
文摘Quantum computers promise to solve finite-temperature properties of quantum many-body systems,which is generally challenging for classical computers due to high computational complexities.Here,we report experimental preparations of Gibbs states and excited states of Heisenberg X X and X X Z models by using a 5-qubit programmable superconducting processor.In the experiments,we apply a hybrid quantum–classical algorithm to generate finite temperature states with classical probability models and variational quantum circuits.We reveal that the Hamiltonians can be fully diagonalized with optimized quantum circuits,which enable us to prepare excited states at arbitrary energy density.We demonstrate that the approach has a self-verifying feature and can estimate fundamental thermal observables with a small statistical error.Based on numerical results,we further show that the time complexity of our approach scales polynomially in the number of qubits,revealing its potential in solving large-scale problems.
基金supported by the following grants: National Basic R&D Program of China (2018YFA0606203)Strategic Priority Research Program of Chinese Academy of Sciences (XDA23090102 and XDA20060501)+2 种基金Guangdong Major Project of Basic and Applied Basic Research (2020B0301030004)Special Fund of China Meteorological Administration for Innovation and Development (CXFZ2021J026)Special Fund for Forecasters of China Meteorological Administration (CMAYBY2020094)。
文摘Convolutional neural networks(CNNs) have been widely studied and found to obtain favorable results in statistical downscaling to derive high-resolution climate variables from large-scale coarse general circulation models(GCMs).However, there is a lack of research exploring the predictor selection for CNN modeling. This paper presents an effective and efficient greedy elimination algorithm to address this problem. The algorithm has three main steps: predictor importance attribution, predictor removal, and CNN retraining, which are performed sequentially and iteratively. The importance of individual predictors is measured by a gradient-based importance metric computed by a CNN backpropagation technique, which was initially proposed for CNN interpretation. The algorithm is tested on the CNN-based statistical downscaling of monthly precipitation with 20 candidate predictors and compared with a correlation analysisbased approach. Linear models are implemented as benchmarks. The experiments illustrate that the predictor selection solution can reduce the number of input predictors by more than half, improve the accuracy of both linear and CNN models,and outperform the correlation analysis method. Although the RMSE(root-mean-square error) is reduced by only 0.8%,only 9 out of 20 predictors are used to build the CNN, and the FLOPs(Floating Point Operations) decrease by 20.4%. The results imply that the algorithm can find subset predictors that correlate more to the monthly precipitation of the target area and seasons in a nonlinear way. It is worth mentioning that the algorithm is compatible with other CNN models with stacked variables as input and has the potential for nonlinear correlation predictor selection.
文摘Phase-matching quantum key distribution is a promising scheme for remote quantum key distribution,breaking through the traditional linear key-rate bound.In practical applications,finite data size can cause significant system performance to deteriorate when data size is below 1010.In this work,an improved statistical fluctuation analysis method is applied for the first time to two decoy-states phase-matching quantum key distribution,offering a new insight and potential solutions for improving the key generation rate and the maximum transmission distance while maintaining security.Moreover,we also compare the influence of the proposed improved statistical fluctuation analysis method on system performance with those of the Gaussian approximation and Chernoff-Hoeffding boundary methods on system performance.The simulation results show that the proposed scheme significantly improves the key generation rate and maximum transmission distance in comparison with the Chernoff-Hoeffding approach,and approach the results obtained when the Gaussian approximation is employed.At the same time,the proposed scheme retains the same security level as the Chernoff-Hoeffding method,and is even more secure than the Gaussian approximation.
基金The authors would like to acknowledge the financial support provided by the National Key R&D Program of China(Grant No.2018YFC1504802)the National Natural Science Foundation of China(Grant Nos.41972266,12102230).
文摘A method for packing irregular particles with a prescribed volume fraction is proposed.Furthermore,the generated granular material adheres to the prescribed statistical distribution and satisfies the desired complex spatial arrangement.First,the irregular geometries of the realistic particles were obtained from the original particle images.Second,the Minkowski sum was used to check the overlap between irregular particles and place an irregular particle in contact with other particles.Third,the optimised advance front method(OAFM)generated irregular particle packing with the prescribed statistical dis-tribution and volume fraction based on the Minkowski sum.Moreover,the signed distance function was introduced to pack the particles in accordance with the desired spatial arrangement.Finally,seven biaxial tests were performed using the UDEC software,which demonstrated the accuracy and potential usefulness of the proposed method.It can model granular material efficiently and reflect the meso-structural characteristics of complex granular materials.This method has a wide range of applications where discrete modelling of granular media is necessary.
基金supported in part by the National Key Research and Development Program of China under Grant 2020YFB1804900in part by the National Natural Science Foundation of China under Grant 92067201,U1805262,62071247,62071249,62171240+2 种基金in part by the Jiangsu Provincial Key Research and Development Program of China under Grant BE2020084-5in part by Special Funds of the Central Government Guiding Local Science and Technology Development under Grant 2021L3010in part by Key provincial scientific and technological innovation projects under Grant 2021G02006.
文摘In this paper,we consider a reconfigurable intelligent surface(RIS)-assisted multiple-input multiple-output(MIMO)secure communication system,where only legitimate user's(Bob's)statistical channel state information(CSI)can be obtained at the transmitter(Alice),while eavesdropper's(Eve's)CSI is unknown.Firstly,the analytical expression of the achievable ergodic rate at Bob is obtained.Then,by exploiting Bob's statistical CSI,we jointly design the transmit covariance matrix at Alice and the phase shift matrix at the RIS to minimize the transmit power of the information signal under the quality-of-service(QoS)constraint of Bob.Finally,we propose an artificial noise(AN)-aided method without Eve's CSI to enhance the security of this system and use the residual power to design the transmit covariance for AN.Simulation results verify the convergence of the proposed method,and also show that there exists a trade-off between the secrecy rate and QoS of Bob.
基金the National Natural Science Foundation of China(Grant Nos.52090032 and 11825203)。
文摘Predicting potential risks associated with the fatigue of key structural components is crucial in engineering design.However,fatigue often involves entangled complexities of material microstructures and service conditions,making diagnosis and prognosis of fatigue damage challenging.We report a statistical learning framework to predict the growth of fatigue cracks and the life-to-failure of the components under loading conditions with uncertainties.Digital libraries of fatigue crack patterns and the remaining life are constructed by high-fidelity physical simulations.Dimensionality reduction and neural network architectures are then used to learn the history dependence and nonlinearity of fatigue crack growth.Path-slicing and re-weighting techniques are introduced to handle the statistical noises and rare events.The predicted fatigue crack patterns are self-updated and self-corrected by the evolving crack patterns.The end-to-end approach is validated by representative examples with fatigue cracks in plates,which showcase the digital-twin scenario in real-time structural health monitoring and fatigue life prediction for maintenance management decision-making.
基金the National Natural Science Foundation of China(NSFC,grant Nos.41874201,12250014,11790302,42174195,and 11873018)the Specialized Research Fund for State Key Laboratories.
文摘Alfvén ion cyclotron waves(ACWs)and kinetic Alfvén waves(KAWs)are found to exist at<0.3 au observed by Parker Solar Probe in Alfvénic slow solar winds.To examine the statistical properties of the background parameters for ACWs and KAWs and related wave disturbances,both wave events observed by Parker Solar Probe are selected and analyzed.The results show that there are obvious differences in the background and disturbance parameters between ACWs and KAWs.ACW events have a relatively higher occurrence rate but with a total duration slightly shorter than KAW events.The median background magnetic field magnitude and the related background solar wind speed of KAW events are larger than those of ACWs.The distributions of the relative disturbances of the proton velocity,proton temperature,the proton number density,andβcover wider ranges for ACW events than for KAW events.The results may be important for the understanding of the nature and characteristics of Alfvénic slow solar wind fluctuations at ion scales near the Sun,and provide the information of the background field and plasma parameters and the wave disturbances of ACWs and KAWs for further relevant theoretical modeling or numerical simulations.
基金funded by the Key R&D project of the Ministry of Science and Technology,China(2017YFC1700604).
文摘Background:Patients with colon cancer who receive chemotherapy usually experience various gastrointestinal adverse reactions,including nausea,vomiting,and diarrhea,which make it challenging for them to adhere to treatment.As an effective traditional Chinese medicine,the Jianpi Bushen formula has been widely used to alleviate the side effects of chemotherapy.Objective:To evaluate the efficacy and safety of Jianpi Bushen formulae for patients who undergo chemotherapy.This statistical analysis plan(SAP)is intended to enhance the transparency and research quality of our randomized controlled trial.Methods:Our study is a multicenter,double-blind,randomized controlled clinical trial.This trial aimed to compare the completion rate of chemotherapy in colon cancer patients who are using and not using Jianpi Bushen formula.To attenuate possible selection bias in the final report,we declared the overall trial design,outcome measures,subgroup analyses,and safety measures.Also,we described the data management and statistical analysis methods in detail.Conclusion:The SAP provides more detailed information than the trial protocol for data management and statistical analysis methods.Further post-hoc analyses can be performed by referring to the SAP,and possible selection bias can be attenuated.
基金supported by the National Natural Science Foun‐dation of China[Grant No.71773061].
文摘The value of a statistical life(VSL)is a crucial tool for monetizing health impacts.To explore the VSL in China,this study examines people’s willingness to pay(WTP)to reduce death risk from air pollution in six representative cities in China based on face-to-face contingent valuation interviews(n=3936)from March 7,2019 to September 30,2019.The results reveal that the WTP varied from CNY 455 to 763 in 2019(USD 66-111),corresponding to a VSL range of CNY 3.79-6.36 million(USD 549395-921940).The VSL in China in 2019 is estimated to be CNY 4.76 million(USD 689659).The statistics indicate that monthly expenditure levels,environmental concerns,risk attitudes,and assumed market acceptance,which have seldom been dis‐cussed in previous studies,significantly impact WTP and VSL.These findings will serve as a reference for ana‐lyzing mortality risk reduction benefits in future research and for policymaking.
文摘Recently,gender equality and women’s entrepreneurship have gained considerable attention in global economic development.Prior to the design of any policy interventions to increase women’s entrepreneurship,it is significant to comprehend the factors motivating women to become entrepreneurs.The non-understanding of the factors can result in the endurance of low living stan-dards and the design of expensive and ineffectual policies.But female involve-ment in entrepreneurship becomes higher in developing economies compared to developed economies.Women Entrepreneurship Index(WEI)plays a vital role in determining the factors that enable theflourishment of high potential female entrepreneurs which enhances economic welfare and contributes to the economic and social fabric of society.Therefore,it is needed to design an automated and accurate WEI prediction model to improve women’s entrepreneurship.In this view,this article develops an automated statistical analysis enabled WEI predic-tive(ASA-WEIP)model.The proposed ASA-WEIP technique aims to effectually determine the WEI.The proposed ASA-WEIP technique encompasses a series of sub-processes such as pre-processing,WEI prediction,and parameter optimiza-tion.For the prediction of WEI,the ASA-WEIP technique makes use of the Deep Belief Network(DBN)model,and the parameter optimization process takes place using Squirrel Search Algorithm(SSA).The performance validation of the ASA-WEIP technique was executed using the benchmark dataset from the Kaggle repo-sitory.The experimental outcomes stated the better outcomes of the ASA-WEIP technique over the other existing techniques.
基金This research was partly supported by the Technology Development Program of MSS[No.S3033853]by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A4A1031509).
文摘Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so on.Statistical data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data.It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves.Thus,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)model.In the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation complexity.For rainfall prediction.Convolution neural network with long short-term memory(CNN-LSTM)technique is exploited.At last,this study involves the pelican optimization algorithm(POA)as a hyperparameter optimizer.The experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive class.The comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.
基金National Natural Science Foundation of China(92044302,41805115)Guangzhou Municipal Science and Technology Project(202002020065)。
文摘The frequent occurrence of dry and hot(DH)days in South China in summer has a negative impact on social development and human health.This study explored the variation characteristics of DH days and the possible reasons for this knotty problem.The findings revealed a notable increase in the number of DH days across most stations,indicating a significant upward trend.Additionally,DH events were observed to occur frequently.The number of DH days increased during 1970-1990,decreased from 1991 to 1997,and stayed stable after 1997.The key climate factors affecting the interannual variability of the number of DH days were the Indian Ocean Basin warming(IOBW)in spring and the East Asian Summer Monsoon(EASM).Compared with the negative phase of IOBW,in the positive phase of IOBW,500 hPa and 850 hPa geopotential height enhanced,the West Pacific subtropical high strengthened and extended abnormally to the west,more solar radiation reached the surface,surface outgoing longwave radiation increased,and there was an anomalous anticyclone in eastern South China.The atmospheric circulation characteristics of the positive and negative phases of ESAM were opposite to those of IOBW,and the abnormal circulation of the positive(negative)phases of ESAM was unfavorable(favorable)for the increase in the number of DH days.A long-term prediction model for the number of summer DH days was established using multiple linear regression,incorporating the key climate factors.The correlation coefficient between the observed and predicted number of DH days was 0.65,and the root-mean-square error was 2.8.In addition,independent forecasts for 2019 showed a deviation of just 1 day.The results of the independent recovery test confirmed the stability of the model,providing evidence that climatic factors did have an impact on DH days in South China.
基金Supported by the National Key R&D Plan(2018YFC1506500)Open Research Fund Project of Key Laboratory of Ecological Environment Meteorology of Qinling Mountains and Loess Plateau of Shaanxi Provincial Meteorological Bureau(2020Y-13)+1 种基金Open Research Fund of Shangluo Key Laboratory of Climate Adaptable City(SLSYS2022007)Shangluo Demonstration Project of Qinling Ecological Monitoring Service System(2020-611002-74-01-006200)。
文摘The study of land surface temperature(LST)is of great significance for ecosystem monitoring and ecological environmental protection in the Qinling Mountains of China.In view of the contradicting spatial and temporal resolutions in extracting LST from satellite remote sensing(RS)data,the areas with complex landforms of the Eastern Qinling Mountains were selected as the research targets to establish the correlation between the normalized difference vegetation index(NDVI)and LST.Detailed information on the surface features and temporal changes in the land surface was provided by Sentinel-2 and Sentinel-3,respectively.Based on the statistically downscaling method,the spatial scale could be decreased from 1000 m to 10 m,and LST with a Sentinel-3 temporal resolution and a 10 m spatial resolution could be retrieved.Comparing the 1 km resolution Sentinel-3 LST with the downscaling results,the 10 m LST downscaling data could accurately reflect the spatial distribution of the thermal characteristics of the original LST image.Moreover,the surface temperature data with a 10 m high spatial resolution had clear texture and obvious geomorphic features that could depict the detailed information of the ground features.The results showed that the average error was 5 K on April 16,2019 and 2.6 K on July 15,2019.The smaller error values indicated the higher vegetation coverage of summer downscaling result with the highest level on July 15.
