Purpose:The aim of this umbrella review was to determine the impact of resistance training(RT)and individual RT prescription variables on muscle mass,strength,and physical function in healthy adults.Methods:Following ...Purpose:The aim of this umbrella review was to determine the impact of resistance training(RT)and individual RT prescription variables on muscle mass,strength,and physical function in healthy adults.Methods:Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,we systematically searched and screened eligible systematic reviews reporting the effects of differing RT prescription variables on muscle mass(or its proxies),strength,and/or physical function in healthy adults aged>18 years.Results:We identified 44 systematic reviews that met our inclusion criteria.The methodological quality of these reviews was assessed using A Measurement Tool to Assess Systematic Reviews;standardized effectiveness statements were generated.We found that RT was consistently a potent stimulus for increasing skeletal muscle mass(4/4 reviews provide some or sufficient evidence),strength(4/6 reviews provided some or sufficient evidence),and physical function(1/1 review provided some evidence).RT load(6/8 reviews provided some or sufficient evidence),weekly frequency(2/4 reviews provided some or sufficient evidence),volume(3/7 reviews provided some or sufficient evidence),and exercise order(1/1 review provided some evidence)impacted RT-induced increases in muscular strength.We discovered that 2/3 reviews provided some or sufficient evidence that RT volume and contraction velocity influenced skeletal muscle mass,while 4/7 reviews provided insufficient evidence in favor of RT load impacting skeletal muscle mass.There was insufficient evidence to conclude that time of day,periodization,inter-set rest,set configuration,set end point,contraction velocity/time under tension,or exercise order(only pertaining to hypertrophy)influenced skeletal muscle adaptations.A paucity of data limited insights into the impact of RT prescription variables on physical function.Conclusion:Overall,RT increased muscle mass,strength,and physical function compared to no exercise.RT intensity(load)and weekly frequency impacted RT-induced increases in muscular strength but not muscle hypertrophy.RT volume(number of sets)influenced muscular strength and hypertrophy.展开更多
Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent ...Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.展开更多
This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative ...This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.展开更多
Plants play an essential role in matter and energy transformations and are key messengers in the carbon and energy cycle. Net primary productivity (NPP) reflects the capability of plants to transform solar energy into...Plants play an essential role in matter and energy transformations and are key messengers in the carbon and energy cycle. Net primary productivity (NPP) reflects the capability of plants to transform solar energy into photosynthesis. It is very sensible for factors affecting on vegetation variability such as climate, soils, plant characteristics and human activities. So, it can be used as an indicator of actual and potential trend of vegetation. In this study we used the actual NPP which was derived from MODIS to assess the response of NPP to climate variables in Gadarif State, from 2000 to 2010. The correlations between NPP and climate variables (temperature and precipitation) are calculated using Pearson’s Correlation Coefficient and ordinary least squares regression. The main results show the following 1) the correlation Coefficient between NPP and mean annual temperature is Somewhat negative for Feshaga, Rahd, Gadarif and Galabat areas and weakly negative in Faw area;2) the correlation Coefficient between NPP and annual total precipitation is weakly negative in Faw, Rahd and Galabat areas and somewhat negative in Galabat and Rahd areas. This study demonstrated that the correlation analysis between NPP and climate variables (precipitation and temperature) gives reliably result of NPP responses to climate variables that is clearly in a very large scale of study area.展开更多
The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the intera...The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.展开更多
To overcome the problem of imprecise and unclear information in the development of quality functions,a method for determining the priority of engineering features based on mixed linguistic variables is proposed.First,...To overcome the problem of imprecise and unclear information in the development of quality functions,a method for determining the priority of engineering features based on mixed linguistic variables is proposed.First,the evaluation member uses the determined linguistic variable to give the correlation strength evaluation matrix of customer requirements and engineering features.Secondly,the relative importance of the evaluation member and customer requirements are aggregated.Finally,the priority of engineering features is obtained by calculating the deviation.The feasibility and practicability of this method are proven by taking the design of a new product of a long bag low-pressure pulse dust collector as an example.展开更多
In order to address the issues of complex system structure and variable selection difficulty for the current heavy haul railway line status evaluation system, a three-category and three-layer heavy-haul line status ev...In order to address the issues of complex system structure and variable selection difficulty for the current heavy haul railway line status evaluation system, a three-category and three-layer heavy-haul line status evaluation variable set construction and reduction optimization method is proposed. Firstly, the status of heavy haul railway line is analyzed, and an initial set of evaluation variables affecting the line status is constructed. Then, based on the association rule and the principal component analysis method, key variables are extracted from the initial variable set to establish the evaluation system. Finally, this method is verified with actual data of a line. The results show that the service performance of heavy haul railway line can still be evaluated accurately when the evaluation variables are reduced by 60% in the proposed method.展开更多
In this article,we establish a general result on complete moment convergence for arrays of rowwise negatively dependent(ND)random variables under the sub-linear expectations.As applications,we can obtain a series of r...In this article,we establish a general result on complete moment convergence for arrays of rowwise negatively dependent(ND)random variables under the sub-linear expectations.As applications,we can obtain a series of results on complete moment convergence for ND random variables under the sub-linear expectations.展开更多
In this paper,we mainly investigate the value distribution of meromorphic functions in Cmwith its partial differential and uniqueness problem on meromorphic functions in Cmand with its k-th total derivative sharing sm...In this paper,we mainly investigate the value distribution of meromorphic functions in Cmwith its partial differential and uniqueness problem on meromorphic functions in Cmand with its k-th total derivative sharing small functions.As an application of the value distribution result,we study the defect relation of a nonconstant solution to the partial differential equation.In particular,we give a connection between the Picard type theorem of Milliox-Hayman and the characterization of entire solutions of a partial differential equation.展开更多
Volume visualization can not only illustrate overall distribution but also inner structure and it is an important approach for space environment research.Space environment simulation can produce several correlated var...Volume visualization can not only illustrate overall distribution but also inner structure and it is an important approach for space environment research.Space environment simulation can produce several correlated variables at the same time.However,existing compressed volume rendering methods only consider reducing the redundant information in a single volume of a specific variable,not dealing with the redundant information among these variables.For space environment volume data with multi-correlated variables,based on the HVQ-1d method we propose a further improved HVQ method by compositing variable-specific levels to reduce the redundant information among these variables.The volume data associated with each variable is divided into disjoint blocks of size 43 initially.The blocks are represented as two levels,a mean level and a detail level.The variable-specific mean levels and detail levels are combined respectively to form a larger global mean level and a larger global detail level.To both global levels,a splitting based on a principal component analysis is applied to compute initial codebooks.Then,LBG algorithm is conducted for codebook refinement and quantization.We further take advantage of progressive rendering based on GPU for real-time interactive visualization.Our method has been tested along with HVQ and HVQ-1d on high-energy proton flux volume data,including>5,>10,>30 and>50 MeV integrated proton flux.The results of our experiments prove that the method proposed in this paper pays the least cost of quality at compression,achieves a higher decompression and rendering speed compared with HVQ and provides satisficed fidelity while ensuring interactive rendering speed.展开更多
This study attempts to investigate the interaction between lower and upper atmosphere, employing daily data of Total Ozone Column (TOC) and atmospheric parameter (cloud cover) over Nigeria from 1998-2012;in order to s...This study attempts to investigate the interaction between lower and upper atmosphere, employing daily data of Total Ozone Column (TOC) and atmospheric parameter (cloud cover) over Nigeria from 1998-2012;in order to study the dynamic effect of ozone on climate and vice versa. This is due to the fact that ozone and climate influence each other and the understanding of the dynamic effect of the interconnectivity is still an open research area. Monthly mean daily TOC and cloud cover data were obtained from the Earth Probe Total Ozone Mass Spectroscopy (EPTOMS) and the International Satellite Cloud Climatology Project (ISCCP)-D2 datasets respectively. Bivariate analysis and Mann Kendall trend tests were used in data analysis. MATLAB and ArcGIS software were employed in analyzing the data. Results reveal that TOC increased spatially from the coastal region to the north eastern region of the country. Seasonally, the highest value of TOC was observed at the peak of rainy season when cloud activity is very high, while the lowest value was recorded in dry season. These variations were attributed to rain producing mechanisms and atmospheric phenomena which influence the transport and distribution of ozone. Furthermore, the statistical analysis reveals significant relationship between TOC and low and middle cloud covers in contrast to high cloud cover. This relationship is consistent with previous studies using other atmospheric variables. This study has given scientific insight which is useful in understanding the coupling of the lower and upper atmosphere.展开更多
In this article, the strong laws of large numbers for array of rowwise asymptotically almost negatively associated(AANA) random variables are studied. Some sufficient conditions for strong laws of large numbers for ar...In this article, the strong laws of large numbers for array of rowwise asymptotically almost negatively associated(AANA) random variables are studied. Some sufficient conditions for strong laws of large numbers for array of rowwise AANA random variables are presented without assumption of identical distribution. Our results extend the corresponding ones for independent random variables to case of AANA random variables.展开更多
In the past few decades,meteorological datasets from remote sensing techniques in agricultural and water resources management have been used by various researchers and managers.Based on the literature,meteorological d...In the past few decades,meteorological datasets from remote sensing techniques in agricultural and water resources management have been used by various researchers and managers.Based on the literature,meteorological datasets are not more accurate than synoptic stations,but their various advantages,such as spatial coverage,time coverage,accessibility,and free use,have made these techniques superior,and sometimes we can use them instead of synoptic stations.In this study,we used four meteorological datasets,including Climatic Research Unit gridded Time Series(CRU TS),Global Precipitation Climatology Centre(GPCC),Agricultural National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications(AgMERRA),Agricultural Climate Forecast System Reanalysis(AgCFSR),to estimate climate variables,i.e.,precipitation,maximum temperature,and minimum temperature,and crop variables,i.e.,reference evapotranspiration,irrigation requirement,biomass,and yield of maize,in Qazvin Province of Iran during 1980-2009.At first,data were gathered from the four meteorological datasets and synoptic station in this province,and climate variables were calculated.Then,after using the AquaCrop model to calculate the crop variables,we compared the results of the synoptic station and meteorological datasets.All the four meteorological datasets showed strong performance for estimating climate variables.AgMERRA and AgCFSR had more accurate estimations for precipitation and maximum temperature.However,their normalized root mean square error was inferior to CRU for minimum temperature.Furthermore,they were all very efficient for estimating the biomass and yield of maize in this province.For reference evapotranspiration and irrigation requirement CRU TS and GPCC were the most efficient rather than AgMERRA and AgCFSR.But for the estimation of biomass and yield,all the four meteorological datasets were reliable.To sum up,GPCC and AgCFSR were the two best datasets in this study.This study suggests the use of meteorological datasets in water resource management and agricultural management to monitor past changes and estimate recent trends.展开更多
Admittedly cognitive variables such as intelligence and aptitude exert great impact on English learning. Affective variables, however, are of intense importance in determining English learning as well, because affect ...Admittedly cognitive variables such as intelligence and aptitude exert great impact on English learning. Affective variables, however, are of intense importance in determining English learning as well, because affect is a starting machine that sets the learning mechanism in motion and learning will run into difficulty if affect does not work properly. Besides, there is mounting interest in exploring the affective domain. Therefore, this paper focuses upon the analyses of four affective variables(attitude, motivation, self-esteem and anxiety) that have bearings on English learning and sets forth Implications for English teaching.展开更多
Objective To examine if the variations at sea level would be able to predict subsequent susceptibility to acute altitude sickness in subjects upon a rapid ascent to high altitude.Methods One hundred and six Han nation...Objective To examine if the variations at sea level would be able to predict subsequent susceptibility to acute altitude sickness in subjects upon a rapid ascent to high altitude.Methods One hundred and six Han nationality male individuals were recruited to this research.Dynamic electrocardiogram,treadmill exercise test,echocardiography,routine blood examination and biochemical analysis were performed when subjects at sea level and entering the plateau respectively.Then multiple regression analysis was performed to construct a multiple linear regression equation using the Lake Louise Score as dependent variable to predict the risk factors at sea level related to acute mountain sickness(AMS).Results Approximately 49.05%of the individuals developed AMS.The tricuspid annular plane systolic excursion(22.0+2.66 vs.23.2+3.19 mm,t=l.998,P=0.048)was significantly lower in the AMS group at sea level,while count of eosinophil[(0.264+0.393)×109/L vs.(0.126+0.084)×109/L,t=-2.040,P—0.045],percentage of diflerences exceeding 50 ms between adjacent normal number of intervals(PNN50,9.66%±5.40%vs.6.98%±5.66%,t=-2.229,P=0.028)and heart rate variability triangle index(57.1+16.1 vs.50.6+12.7,t=-2.271,P=0.025)were significantly higher.After acute exposure to high altitude,C-reactive protein(0.098+0.103 vs.0.062+0.045 g/L,t=-2.132,P=0.037),aspartate aminotransferase(19.7+6.7275.17,3±3.95 U/L,t=-2.231,P=0.028)and creatinine(85.1±12.9 vs.77.7±11.2 mmol/L,t=3.162,P=0.002)were significantly higher in the AMS group,while alkaline phosphatase(71.7+18.2 vs.80.6+20.2 U/L,t=2.389,P=0.019),standard deviation of normal-to-normal RR intervals(126.5+35.9 vs.143.3+36.4 ms,t—2.320,P—0.022),ejection time(276.9+50.8 vs.313.8+48.9 ms,t—3.641,P—0.001)and heart rate variability triangle index(37.1+12.9 vs.41.9+11.1,t=2.O2O,P=0.047)were significantly lower.Using the Lake Louise Score as the dependent variable,prediction equation were established to estimate AMS:Lake Louise Score=3.783+0.281Xeosinophil-0.219Xalkaline phosphatase+O.O32XPNN50.Conclusions We elucidated the differences of pl^siological variables as well as noninvasive cardiovascular indicators for subjects after high altitude exposure compared with those at sea level.We also created an acute high altitude reaction early warning equation based on the physiological variables and noninvasive cardiovascular indicators at sea level.展开更多
In this paper, systematic studies on the changes in concentrations of the environmental factors and the net-phytoplankton community, and the relationship between them in the Liaodong Bay, Bohai Sea during 2013 are pre...In this paper, systematic studies on the changes in concentrations of the environmental factors and the net-phytoplankton community, and the relationship between them in the Liaodong Bay, Bohai Sea during 2013 are presented. The PCA results showed that higher levels of nutrients and dissolved heavy metals in the river-estuary-bay system were closely related to the river runoff. Since the influences of industrial and anthropogenic activities, the Liaodong Bay coastal areas are facing a huge environmental challenge of nutrients and heavy metal pollution. Net-phytoplankton community structure showed obvious seasonal succession, among which the dominant and(or) key species were the main factors affecting community structure change and stability. Under certain environmental conditions, the dominant species and(or) key species dominated the phytoplankton community structure succession. The Bio-ENV results suggested that the seawater temperature, nutrient, Cu, Pb, Zn, and Cd in Liaodong Bay are important environmental variables that affect the phytoplankton community structure. Anthropogenic activities have significantly contributed to the changes in concentrations of environmental factors and the net-phytoplankton community structure and stability, and the relationship between them.展开更多
In the paper, the strong convergence properties for two different weighted sums of negatively orthant dependent(NOD) random variables are investigated. Let {X_n, n ≥ 1}be a sequence of NOD random variables. The resul...In the paper, the strong convergence properties for two different weighted sums of negatively orthant dependent(NOD) random variables are investigated. Let {X_n, n ≥ 1}be a sequence of NOD random variables. The results obtained in the paper generalize the corresponding ones for i.i.d. random variables and identically distributed NA random variables to the case of NOD random variables, which are stochastically dominated by a random variable X. As a byproduct, the Marcinkiewicz-Zygmund type strong law of large numbers for NOD random variables is also obtained.展开更多
The fatigue life of aeroengine turbine disc presents great dispersion due to the randomness of the basic variables,such as applied load,working temperature,geometrical dimensions and material properties.In order to am...The fatigue life of aeroengine turbine disc presents great dispersion due to the randomness of the basic variables,such as applied load,working temperature,geometrical dimensions and material properties.In order to ameliorate reliability analysis efficiency without loss of reliability,the distributed collaborative response surface method(DCRSM) was proposed,and its basic theories were established in this work.Considering the failure dependency among the failure modes,the distributed response surface was constructed to establish the relationship between the failure mode and the relevant random variables.Then,the failure modes were considered as the random variables of system response to obtain the distributed collaborative response surface model based on structure failure criterion.Finally,the given turbine disc structure was employed to illustrate the feasibility and validity of the presented method.Through the comparison of DCRSM,Monte Carlo method(MCM) and the traditional response surface method(RSM),the results show that the computational precision for DCRSM is more consistent with MCM than RSM,while DCRSM needs far less computing time than MCM and RSM under the same simulation conditions.Thus,DCRSM is demonstrated to be a feasible and valid approach for improving the computational efficiency of reliability analysis for aeroengine turbine disc fatigue life with multiple random variables,and has great potential value for the complicated mechanical structure with multi-component and multi-failure mode.展开更多
In this paper, we will present some strong convergence results for sequences of ψ-mixing random variables. The results for sequences of ψ-mixing random variables generalize the corresponding results for independent ...In this paper, we will present some strong convergence results for sequences of ψ-mixing random variables. The results for sequences of ψ-mixing random variables generalize the corresponding results for independent random variable sequences without any extra conditions.展开更多
基金suppoited by an Alexander Graliam Bell Canada Graduate Scholarship-Doctoralsupported by an Ontario Graduate Scholarshipsupported by the Canada Research Chairs programme。
文摘Purpose:The aim of this umbrella review was to determine the impact of resistance training(RT)and individual RT prescription variables on muscle mass,strength,and physical function in healthy adults.Methods:Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,we systematically searched and screened eligible systematic reviews reporting the effects of differing RT prescription variables on muscle mass(or its proxies),strength,and/or physical function in healthy adults aged>18 years.Results:We identified 44 systematic reviews that met our inclusion criteria.The methodological quality of these reviews was assessed using A Measurement Tool to Assess Systematic Reviews;standardized effectiveness statements were generated.We found that RT was consistently a potent stimulus for increasing skeletal muscle mass(4/4 reviews provide some or sufficient evidence),strength(4/6 reviews provided some or sufficient evidence),and physical function(1/1 review provided some evidence).RT load(6/8 reviews provided some or sufficient evidence),weekly frequency(2/4 reviews provided some or sufficient evidence),volume(3/7 reviews provided some or sufficient evidence),and exercise order(1/1 review provided some evidence)impacted RT-induced increases in muscular strength.We discovered that 2/3 reviews provided some or sufficient evidence that RT volume and contraction velocity influenced skeletal muscle mass,while 4/7 reviews provided insufficient evidence in favor of RT load impacting skeletal muscle mass.There was insufficient evidence to conclude that time of day,periodization,inter-set rest,set configuration,set end point,contraction velocity/time under tension,or exercise order(only pertaining to hypertrophy)influenced skeletal muscle adaptations.A paucity of data limited insights into the impact of RT prescription variables on physical function.Conclusion:Overall,RT increased muscle mass,strength,and physical function compared to no exercise.RT intensity(load)and weekly frequency impacted RT-induced increases in muscular strength but not muscle hypertrophy.RT volume(number of sets)influenced muscular strength and hypertrophy.
文摘Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.
基金supported by the National Natural Science Foundation of China (62073303,61673356)Hubei Provincial Natural Science Foundation of China (2015CFA010)the 111 Project(B17040)。
文摘This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.
文摘Plants play an essential role in matter and energy transformations and are key messengers in the carbon and energy cycle. Net primary productivity (NPP) reflects the capability of plants to transform solar energy into photosynthesis. It is very sensible for factors affecting on vegetation variability such as climate, soils, plant characteristics and human activities. So, it can be used as an indicator of actual and potential trend of vegetation. In this study we used the actual NPP which was derived from MODIS to assess the response of NPP to climate variables in Gadarif State, from 2000 to 2010. The correlations between NPP and climate variables (temperature and precipitation) are calculated using Pearson’s Correlation Coefficient and ordinary least squares regression. The main results show the following 1) the correlation Coefficient between NPP and mean annual temperature is Somewhat negative for Feshaga, Rahd, Gadarif and Galabat areas and weakly negative in Faw area;2) the correlation Coefficient between NPP and annual total precipitation is weakly negative in Faw, Rahd and Galabat areas and somewhat negative in Galabat and Rahd areas. This study demonstrated that the correlation analysis between NPP and climate variables (precipitation and temperature) gives reliably result of NPP responses to climate variables that is clearly in a very large scale of study area.
基金supported in part by the Central Government Guides Local Science and TechnologyDevelopment Funds(Grant No.YDZJSX2021A038)in part by theNational Natural Science Foundation of China under(Grant No.61806138)in part by the China University Industry-University-Research Collaborative Innovation Fund(Future Network Innovation Research and Application Project)(Grant 2021FNA04014).
文摘The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.
文摘To overcome the problem of imprecise and unclear information in the development of quality functions,a method for determining the priority of engineering features based on mixed linguistic variables is proposed.First,the evaluation member uses the determined linguistic variable to give the correlation strength evaluation matrix of customer requirements and engineering features.Secondly,the relative importance of the evaluation member and customer requirements are aggregated.Finally,the priority of engineering features is obtained by calculating the deviation.The feasibility and practicability of this method are proven by taking the design of a new product of a long bag low-pressure pulse dust collector as an example.
文摘In order to address the issues of complex system structure and variable selection difficulty for the current heavy haul railway line status evaluation system, a three-category and three-layer heavy-haul line status evaluation variable set construction and reduction optimization method is proposed. Firstly, the status of heavy haul railway line is analyzed, and an initial set of evaluation variables affecting the line status is constructed. Then, based on the association rule and the principal component analysis method, key variables are extracted from the initial variable set to establish the evaluation system. Finally, this method is verified with actual data of a line. The results show that the service performance of heavy haul railway line can still be evaluated accurately when the evaluation variables are reduced by 60% in the proposed method.
基金the National Natural Science Foundation of China(71871046,11661029)Natural Science Foundation of Guangxi(2018JJB110010)。
文摘In this article,we establish a general result on complete moment convergence for arrays of rowwise negatively dependent(ND)random variables under the sub-linear expectations.As applications,we can obtain a series of results on complete moment convergence for ND random variables under the sub-linear expectations.
基金partially supported by the NSFC(11271227,11271161)the PCSIRT(IRT1264)the Fundamental Research Funds of Shandong University(2017JC019)。
文摘In this paper,we mainly investigate the value distribution of meromorphic functions in Cmwith its partial differential and uniqueness problem on meromorphic functions in Cmand with its k-th total derivative sharing small functions.As an application of the value distribution result,we study the defect relation of a nonconstant solution to the partial differential equation.In particular,we give a connection between the Picard type theorem of Milliox-Hayman and the characterization of entire solutions of a partial differential equation.
基金the Key Research Program of the Chinese Academy of Sciences(ZDRE-KT-2021-3)。
文摘Volume visualization can not only illustrate overall distribution but also inner structure and it is an important approach for space environment research.Space environment simulation can produce several correlated variables at the same time.However,existing compressed volume rendering methods only consider reducing the redundant information in a single volume of a specific variable,not dealing with the redundant information among these variables.For space environment volume data with multi-correlated variables,based on the HVQ-1d method we propose a further improved HVQ method by compositing variable-specific levels to reduce the redundant information among these variables.The volume data associated with each variable is divided into disjoint blocks of size 43 initially.The blocks are represented as two levels,a mean level and a detail level.The variable-specific mean levels and detail levels are combined respectively to form a larger global mean level and a larger global detail level.To both global levels,a splitting based on a principal component analysis is applied to compute initial codebooks.Then,LBG algorithm is conducted for codebook refinement and quantization.We further take advantage of progressive rendering based on GPU for real-time interactive visualization.Our method has been tested along with HVQ and HVQ-1d on high-energy proton flux volume data,including>5,>10,>30 and>50 MeV integrated proton flux.The results of our experiments prove that the method proposed in this paper pays the least cost of quality at compression,achieves a higher decompression and rendering speed compared with HVQ and provides satisficed fidelity while ensuring interactive rendering speed.
文摘This study attempts to investigate the interaction between lower and upper atmosphere, employing daily data of Total Ozone Column (TOC) and atmospheric parameter (cloud cover) over Nigeria from 1998-2012;in order to study the dynamic effect of ozone on climate and vice versa. This is due to the fact that ozone and climate influence each other and the understanding of the dynamic effect of the interconnectivity is still an open research area. Monthly mean daily TOC and cloud cover data were obtained from the Earth Probe Total Ozone Mass Spectroscopy (EPTOMS) and the International Satellite Cloud Climatology Project (ISCCP)-D2 datasets respectively. Bivariate analysis and Mann Kendall trend tests were used in data analysis. MATLAB and ArcGIS software were employed in analyzing the data. Results reveal that TOC increased spatially from the coastal region to the north eastern region of the country. Seasonally, the highest value of TOC was observed at the peak of rainy season when cloud activity is very high, while the lowest value was recorded in dry season. These variations were attributed to rain producing mechanisms and atmospheric phenomena which influence the transport and distribution of ozone. Furthermore, the statistical analysis reveals significant relationship between TOC and low and middle cloud covers in contrast to high cloud cover. This relationship is consistent with previous studies using other atmospheric variables. This study has given scientific insight which is useful in understanding the coupling of the lower and upper atmosphere.
基金Supported by the National Natural Science Foundation of China（lilT1001, 11201001） Supported by the Natural Science Foundation of Anhui Province（1208085QA03）+1 种基金 Supported by the Talents Youth Fund of Anhui Province Universities（2012SQRL204） Supported by th Doctoral Research Start-up Funds Projects of Anhui University（33190250）
文摘In this article, the strong laws of large numbers for array of rowwise asymptotically almost negatively associated(AANA) random variables are studied. Some sufficient conditions for strong laws of large numbers for array of rowwise AANA random variables are presented without assumption of identical distribution. Our results extend the corresponding ones for independent random variables to case of AANA random variables.
文摘In the past few decades,meteorological datasets from remote sensing techniques in agricultural and water resources management have been used by various researchers and managers.Based on the literature,meteorological datasets are not more accurate than synoptic stations,but their various advantages,such as spatial coverage,time coverage,accessibility,and free use,have made these techniques superior,and sometimes we can use them instead of synoptic stations.In this study,we used four meteorological datasets,including Climatic Research Unit gridded Time Series(CRU TS),Global Precipitation Climatology Centre(GPCC),Agricultural National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications(AgMERRA),Agricultural Climate Forecast System Reanalysis(AgCFSR),to estimate climate variables,i.e.,precipitation,maximum temperature,and minimum temperature,and crop variables,i.e.,reference evapotranspiration,irrigation requirement,biomass,and yield of maize,in Qazvin Province of Iran during 1980-2009.At first,data were gathered from the four meteorological datasets and synoptic station in this province,and climate variables were calculated.Then,after using the AquaCrop model to calculate the crop variables,we compared the results of the synoptic station and meteorological datasets.All the four meteorological datasets showed strong performance for estimating climate variables.AgMERRA and AgCFSR had more accurate estimations for precipitation and maximum temperature.However,their normalized root mean square error was inferior to CRU for minimum temperature.Furthermore,they were all very efficient for estimating the biomass and yield of maize in this province.For reference evapotranspiration and irrigation requirement CRU TS and GPCC were the most efficient rather than AgMERRA and AgCFSR.But for the estimation of biomass and yield,all the four meteorological datasets were reliable.To sum up,GPCC and AgCFSR were the two best datasets in this study.This study suggests the use of meteorological datasets in water resource management and agricultural management to monitor past changes and estimate recent trends.
文摘Admittedly cognitive variables such as intelligence and aptitude exert great impact on English learning. Affective variables, however, are of intense importance in determining English learning as well, because affect is a starting machine that sets the learning mechanism in motion and learning will run into difficulty if affect does not work properly. Besides, there is mounting interest in exploring the affective domain. Therefore, this paper focuses upon the analyses of four affective variables(attitude, motivation, self-esteem and anxiety) that have bearings on English learning and sets forth Implications for English teaching.
基金National Science and Technology Major Projects for Major New Drugs Innovation and Development(2014ZX09J14102-02A)Special Topic on Military Health Care(17bjz41)National Natural Science Foundation of China(81170249 and 30700305).
文摘Objective To examine if the variations at sea level would be able to predict subsequent susceptibility to acute altitude sickness in subjects upon a rapid ascent to high altitude.Methods One hundred and six Han nationality male individuals were recruited to this research.Dynamic electrocardiogram,treadmill exercise test,echocardiography,routine blood examination and biochemical analysis were performed when subjects at sea level and entering the plateau respectively.Then multiple regression analysis was performed to construct a multiple linear regression equation using the Lake Louise Score as dependent variable to predict the risk factors at sea level related to acute mountain sickness(AMS).Results Approximately 49.05%of the individuals developed AMS.The tricuspid annular plane systolic excursion(22.0+2.66 vs.23.2+3.19 mm,t=l.998,P=0.048)was significantly lower in the AMS group at sea level,while count of eosinophil[(0.264+0.393)×109/L vs.(0.126+0.084)×109/L,t=-2.040,P—0.045],percentage of diflerences exceeding 50 ms between adjacent normal number of intervals(PNN50,9.66%±5.40%vs.6.98%±5.66%,t=-2.229,P=0.028)and heart rate variability triangle index(57.1+16.1 vs.50.6+12.7,t=-2.271,P=0.025)were significantly higher.After acute exposure to high altitude,C-reactive protein(0.098+0.103 vs.0.062+0.045 g/L,t=-2.132,P=0.037),aspartate aminotransferase(19.7+6.7275.17,3±3.95 U/L,t=-2.231,P=0.028)and creatinine(85.1±12.9 vs.77.7±11.2 mmol/L,t=3.162,P=0.002)were significantly higher in the AMS group,while alkaline phosphatase(71.7+18.2 vs.80.6+20.2 U/L,t=2.389,P=0.019),standard deviation of normal-to-normal RR intervals(126.5+35.9 vs.143.3+36.4 ms,t—2.320,P—0.022),ejection time(276.9+50.8 vs.313.8+48.9 ms,t—3.641,P—0.001)and heart rate variability triangle index(37.1+12.9 vs.41.9+11.1,t=2.O2O,P=0.047)were significantly lower.Using the Lake Louise Score as the dependent variable,prediction equation were established to estimate AMS:Lake Louise Score=3.783+0.281Xeosinophil-0.219Xalkaline phosphatase+O.O32XPNN50.Conclusions We elucidated the differences of pl^siological variables as well as noninvasive cardiovascular indicators for subjects after high altitude exposure compared with those at sea level.We also created an acute high altitude reaction early warning equation based on the physiological variables and noninvasive cardiovascular indicators at sea level.
基金supported by the National Natural Science Foundation of China(No.31400406)the Public Science and Technology Research Funds Projects of Ocean(No.201505019)+1 种基金the Natural Science Foundation of Liaoning Province under contract(No.201402 0182)the Science and Technology Project of Liaoning Province(No.2015103044)
文摘In this paper, systematic studies on the changes in concentrations of the environmental factors and the net-phytoplankton community, and the relationship between them in the Liaodong Bay, Bohai Sea during 2013 are presented. The PCA results showed that higher levels of nutrients and dissolved heavy metals in the river-estuary-bay system were closely related to the river runoff. Since the influences of industrial and anthropogenic activities, the Liaodong Bay coastal areas are facing a huge environmental challenge of nutrients and heavy metal pollution. Net-phytoplankton community structure showed obvious seasonal succession, among which the dominant and(or) key species were the main factors affecting community structure change and stability. Under certain environmental conditions, the dominant species and(or) key species dominated the phytoplankton community structure succession. The Bio-ENV results suggested that the seawater temperature, nutrient, Cu, Pb, Zn, and Cd in Liaodong Bay are important environmental variables that affect the phytoplankton community structure. Anthropogenic activities have significantly contributed to the changes in concentrations of environmental factors and the net-phytoplankton community structure and stability, and the relationship between them.
基金Supported by the National Natural Science Foundation of China(11671012,11501004,11501005)the Natural Science Foundation of Anhui Province(1508085J06)+2 种基金the Key Projects for Academic Talent of Anhui Province(gxbj ZD2016005)the Quality Engineering Project of Anhui Province(2016jyxm0047)the Graduate Academic Innovation Research Project of Anhui University(yfc100004)
文摘In the paper, the strong convergence properties for two different weighted sums of negatively orthant dependent(NOD) random variables are investigated. Let {X_n, n ≥ 1}be a sequence of NOD random variables. The results obtained in the paper generalize the corresponding ones for i.i.d. random variables and identically distributed NA random variables to the case of NOD random variables, which are stochastically dominated by a random variable X. As a byproduct, the Marcinkiewicz-Zygmund type strong law of large numbers for NOD random variables is also obtained.
基金Project(51335003)supported by the National Natural Science Foundation of ChinaProject(20111102110011)supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China
文摘The fatigue life of aeroengine turbine disc presents great dispersion due to the randomness of the basic variables,such as applied load,working temperature,geometrical dimensions and material properties.In order to ameliorate reliability analysis efficiency without loss of reliability,the distributed collaborative response surface method(DCRSM) was proposed,and its basic theories were established in this work.Considering the failure dependency among the failure modes,the distributed response surface was constructed to establish the relationship between the failure mode and the relevant random variables.Then,the failure modes were considered as the random variables of system response to obtain the distributed collaborative response surface model based on structure failure criterion.Finally,the given turbine disc structure was employed to illustrate the feasibility and validity of the presented method.Through the comparison of DCRSM,Monte Carlo method(MCM) and the traditional response surface method(RSM),the results show that the computational precision for DCRSM is more consistent with MCM than RSM,while DCRSM needs far less computing time than MCM and RSM under the same simulation conditions.Thus,DCRSM is demonstrated to be a feasible and valid approach for improving the computational efficiency of reliability analysis for aeroengine turbine disc fatigue life with multiple random variables,and has great potential value for the complicated mechanical structure with multi-component and multi-failure mode.
基金Supported by the University Students Science Research Training Program of Anhui University(KYXL20110004)
文摘In this paper, we will present some strong convergence results for sequences of ψ-mixing random variables. The results for sequences of ψ-mixing random variables generalize the corresponding results for independent random variable sequences without any extra conditions.
