From economy to political administrations, education to health, environment to human rights, many problems we met have gained a global importance in recent days. Existing state systems, political parties and nation st...From economy to political administrations, education to health, environment to human rights, many problems we met have gained a global importance in recent days. Existing state systems, political parties and nation states are not adequate for solving these problems in question effectively on their own. Not only governments and local authorities but also voluntary organizations based on completely voluntary activities have significant roles in solving these problems. Effective performance of voluntary organizations depends on increasing volunteer population. Individuals＇ attitudes or their perception of understanding volunteerism play an important role in their contributions to voluntary organizations. The aim of this study is to determine individuals＇ ways of perceiving volunteerism concept and their tendency towards it. Furthermore, differences between men and women＇s perception and attitudes towards volunteerism concept have been examined. For this purpose, a survey has been conducted over university students of bachelor＇s degree. Tendencies and attitudes towards volunteerism compared to gender differences have been tested via logistic regression method. Research results reveal that women take part in voluntary activities more than men and women perceive volunteerism as ＂a political position＂ while men perceive volunteerism as ＂a learning atmosphere and learning process＂.展开更多
A complex terrain and topography resulted in an enormous landslide-dammed area northeast of Afghanistan. Moreover, debris, rock avalanches, and landslides occurrences are the primary source of lakes created within the...A complex terrain and topography resulted in an enormous landslide-dammed area northeast of Afghanistan. Moreover, debris, rock avalanches, and landslides occurrences are the primary source of lakes created within the area. Recently, instances have increased because of the high displacement and mass movement by glacial and seismic activities. In this study, using GIS and R statistical software, we performed a logistic regression modeling in order to map and predict the probability of landslides-dammed occurrences. Totally, 361 lakes were mapped using Google Earth historical imagery. This total was divided into 253 （70%） lakes for modeling and 801 （30%） lakes for the model validation. They were randomly selected by creating a fishnet for the study area using Arc toolbox in GIS. Four independent variables that are mostly contributed to landslide-dammed occurrences consisting of slope angles, relief classes, distances to major water sources and earthquake epicenters, were extracted from DEM （digital elevation model） data using 85-meter resolution. The result is a grid map that classified the area into Low （16,834.98 km2）, Medium （2,217.302 kin：） and High （2,013.55 km2） vulnerability to landslide-dammed occurrences. Overall, the model result has been validated by using a ROC （receiver operator characteristic） curve available in SPSS software. The model validation showed a 95.1 percent prediction accuracy that is considered satisfactory.展开更多
Combinatorial drug therapies are generally more effective than monotherapies in treating viral infections.However,it is critical for dose optimization to maximize the efficacy and minimize side effects.Although variou...Combinatorial drug therapies are generally more effective than monotherapies in treating viral infections.However,it is critical for dose optimization to maximize the efficacy and minimize side effects.Although various strategies have been devised to accelerate the optimization process,their efficiencies were limited by the high noises and suboptimal reproducibility of biological assays.With conventional methods,variances among the replications are used to evaluate the errors of the readouts alone rather than actively participating in the optimization.Herein,we present the Regression Modeling Enabled by Monte Carlo Method(ReMEMC)algorithm for rapid identification of effective combinational therapies.ReMEMC transforms the sample variations into probability distributions of the regression coefficients and predictions.In silico simulations revealed that ReMEMC outperformed conventional regression methods in benchmark problems,and demonstrated its superior robustness against experimental noises.Using COVID-19 as a model disease,ReMEMC successfully identified an optimal 3-drug combination among 10 anti-SARS-CoV-2 drug compounds within two rounds of experiments.The optimal combination showed 2-log and 3-log higher load reduction than non-optimized combinations and monotherapy,respectively.Further workflow refinement allowed identification of personalized drug combinational therapies within 5 days.The strategy may serve as an efficient and universal tool for dose combination optimization.展开更多
Data-driven regression models are generally calibrated by minimizing a representation error.However,opti-mizing the model accuracy may create nonphysical wiggles.In this study,we propose topological consistency as a n...Data-driven regression models are generally calibrated by minimizing a representation error.However,opti-mizing the model accuracy may create nonphysical wiggles.In this study,we propose topological consistency as a new metric to mitigate these wiggles.The key enabler is Persistent Data Topology(PDT)which extracts a topological skeleton from discrete scalar field data.PDT identifies the extrema of the model based on a neighborhood analysis.The topological error is defined as the mismatch of extrema between the data and the model.The methodology is exemplified for the modeling of the Laminar Burning Velocity(LBV)of ammonia-hydrogen flames.Four regression models,Multi-layer Perceptron(MLP),eXtreme Gradient Boosting(XGBoost),Random Forest(RF),and Light Gradient Boosting Machine(Light GBM),are trained using the data generated by a modified GRI3.0 mechanism.In comparison,MLP builds a model that achieves the highest accuracy and preserves the topological structure of the data.We expect that the proposed topologically consistent regression modeling will enjoy many more applications in model calibration,model selection and optimization algorithms.展开更多
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
BACKGROUND Paternal perinatal depression(PPD)is closely associated with maternal mental health challenges,marital strain,and adverse child developmental outcomes.Despite its significant impact,PPD remains under-recogn...BACKGROUND Paternal perinatal depression(PPD)is closely associated with maternal mental health challenges,marital strain,and adverse child developmental outcomes.Despite its significant impact,PPD remains under-recognized in family-centered clinical practice.Concurrently,against the backdrop of rising rates of delayed marriage and China’s Maternity Incentive Policy,the proportion of women giving birth at an advanced maternal age is increasing.Nevertheless,research specifically examining PPD among spouses of older mothers remains critically scarce,both in China and globally.AIM To investigate PPD and its influencing factors in Chinese advanced maternal age families.METHODS This cross-sectional study included 358 participants;it was conducted among fathers of pregnant women of advanced maternal age at five hospitals in the Pearl River Delta region of China from September 2023 to June 2024.Data were collected via a general information questionnaire,the Social Support Rating Scale,and the Edinburgh Postnatal Depression Scale.Latent profile analysis and regression mixture models(RMMs)were adopted to analyze the latent PPD types and factors that influenced PPD.RESULTS The incidence of PPD was 16.48%,and three profiles were identified:Low-symptomatic(175 cases,48.89%),monophasic(140 cases,39.10%),and high-symptomatic(43 cases,12.01%).The RMM analysis revealed that first pregnancy,low income(<¥3000/month),part-time work,and a history of abnormal pregnancy were positively associated with the high-symptomatic type(P<0.05).Conversely,high subjective support and support utilization were negatively associated with the high-symptomatic type compared with the low-symptomatic type(P<0.05).Good couple relationships,high objective and subjective support,and high support utilization were negatively associated with monophasic disorder(P<0.05).CONCLUSION PPD incidence is high among Chinese fathers with advanced maternal age partners,and the characteristics of depression are varied.Healthcare practitioners should prioritize individuals with low levels of social support.展开更多
The energy sector in Poland is the source of 81% of greenhouse gas（GHG） emissions. Poland,among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector activ...The energy sector in Poland is the source of 81% of greenhouse gas（GHG） emissions. Poland,among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO2 emissions demonstrate a high relationship（0.97） with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH4 emission, indicated the presence of hard coal（0.66）, peat and fuel wood（0.34）, solid waste fuels, as well as other sources（- 0.64） as the most important variables. The adjusted coefficient is suitable and equals R2= 0.90. For N2 O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N2 O emission is the peat and wood fuel consumption.展开更多
The results of mass appraisal in many countries are used as a basis for calculating the amount of real estate tax,therefore,regardless of the methods used to calculate it,the resulting value should be as close as poss...The results of mass appraisal in many countries are used as a basis for calculating the amount of real estate tax,therefore,regardless of the methods used to calculate it,the resulting value should be as close as possible to the market value of the real estate to maintain a balance of interests between the state and the rights holders.In practice,this condition is not always met,since,firstly,the quality of market data is often very low,and secondly,some markets are characterized by low activity,which is expressed in a deficit of information on asking prices.The aim of the work is ecological valuation of land use:how regression-based mass appraisal can inform ecological conservation,land degradation,and sustainable land management.Four multiple regression models were constructed for AI generated map of land plots for recreational use in St.Petersburg(Russia)with different volumes of market information(32,30,20 and 15 units of market information with four price-forming factors).During the analysis of the quality of the models,it was revealed that the best result is shown by the model built on the maximum sample size,then the model based on 15 analogs,which proves that a larger number of analog objects does not always allow us to achieve better results,since the more analog objects there are.展开更多
This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 199...This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.展开更多
The soil water status was investigated under soil surface mulching techniques and two drip line depths from the soil surface(DL).These techniques were black plastic film(BPF),palm tree waste(PTW),and no mulching(NM)as...The soil water status was investigated under soil surface mulching techniques and two drip line depths from the soil surface(DL).These techniques were black plastic film(BPF),palm tree waste(PTW),and no mulching(NM)as the control treatment.The DL were 15 cm and 25 cm,with surface drip irrigation used as the control.The results indicated that both the BPF and PTW mulching enhanced the soil water retention capacity and there was about 6%water saving in subsurface drip irrigation,compared with NM.Furthermore,the water savings at a DL of 25 cm were lower(15-20 mm)than those at a DL of 15 cm(19-24 mm),whereas surface drip irrigation consumed more water.The distribution of soil water content(θv)for BPF and PTW were more useful than for NM.Hence,mulching the soil with PTW is recommended due to the lower costs and using a DL of 15 cm.Theθv values were derived using multiple linear regression(MLR)and multiple nonlinear regression(MNLR)models.Multiple regression analysis revealed the superiority of the MLR over the MNLR model,which in the training and testing processes had coefficients of correlation of 0.86 and 0.88,root mean square errors of 0.37 and 0.35,and indices of agreement of 0.99 and 0.93,respectively,over the MNLR model.Moreover,DL and spacing from the drip line had a significant effect on the estimation of θv.展开更多
Cyber losses in terms of number of records breached under cyber incidents commonly feature a significant portion of zeros, specific characteristics of mid-range losses and large losses, which make it hard to model the...Cyber losses in terms of number of records breached under cyber incidents commonly feature a significant portion of zeros, specific characteristics of mid-range losses and large losses, which make it hard to model the whole range of the losses using a standard loss distribution. We tackle this modeling problem by proposing a three-component spliced regression model that can simultaneously model zeros, moderate and large losses and consider heterogeneous effects in mixture components. To apply our proposed model to Privacy Right Clearinghouse (PRC) data breach chronology, we segment geographical groups using unsupervised cluster analysis, and utilize a covariate-dependent probability to model zero losses, finite mixture distributions for moderate body and an extreme value distribution for large losses capturing the heavy-tailed nature of the loss data. Parameters and coefficients are estimated using the Expectation-Maximization (EM) algorithm. Combining with our frequency model (generalized linear mixed model) for data breaches, aggregate loss distributions are investigated and applications on cyber insurance pricing and risk management are discussed.展开更多
The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysi...The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example.展开更多
A fuzzy observations-based radial basis function neural network （FORBFNN） is presented for modeling nonlinear systems in which the observations of response are imprecise but can be represented as fuzzy membership fu...A fuzzy observations-based radial basis function neural network （FORBFNN） is presented for modeling nonlinear systems in which the observations of response are imprecise but can be represented as fuzzy membership functions. In the FORBFNN model, the weight coefficients of nodes in the hidden layer are identified by using the fuzzy expectation-maximization （ EM ） algorithm, whereas the optimal number of these nodes as well as the centers and widths of radial basis functions are automatically constructed by using a data-driven method. Namely, the method starts with an initial node, and then a new node is added in a hidden layer according to some rules. This procedure is not terminated until the model meets the preset requirements. The method considers both the accuracy and complexity of the model. Numerical simulation results show that the modeling method is effective, and the established model has high prediction accuracy.展开更多
Under-fitting problems usually occur in regression models for dam safety monitoring.To overcome the local convergence of the regression, a genetic algorithm (GA) was proposed using a real parameter coding, a ranking s...Under-fitting problems usually occur in regression models for dam safety monitoring.To overcome the local convergence of the regression, a genetic algorithm (GA) was proposed using a real parameter coding, a ranking selection operator, an arithmetical crossover operator and a uniform mutation operator, and calculated the least-square error of the observed and computed values as its fitness function. The elitist strategy was used to improve the speed of the convergence. After that, the modified genetic algorithm was applied to reassess the coefficients of the regression model and a genetic regression model was set up. As an example, a slotted gravity dam in the Northeast of China was introduced. The computational results show that the genetic regression model can solve the under-fitting problems perfectly.展开更多
Because of the relativity among the parameters, partial least square regression(PLSR)was applied to build the model and get the regression equation. The improved algorithm simplified the calculating process greatly be...Because of the relativity among the parameters, partial least square regression(PLSR)was applied to build the model and get the regression equation. The improved algorithm simplified the calculating process greatly because of the reduction of calculation. The orthogonal design was adopted in this experiment. Every sample had strong representation, which could reduce the experimental time and obtain the overall test data. Combined with the formation problem of gas metal arc weld with big current, the auxiliary analysis technique of PLSR was discussed and the regression equation of form factors (i.e. surface width, weld penetration and weld reinforcement) to process parameters(i.e. wire feed rate, wire extension, welding speed, gas flow, welding voltage and welding current)was given. The correlativity structure among variables was analyzed and there was certain correlation between independent variables matrix X and dependent variables matrix Y. The regression analysis shows that the welding speed mainly influences the weld formation while the variation of gas flow in certain range has little influence on formation of weld. The fitting plot of regression accuracy is given. The fitting quality of regression equation is basically satisfactory.展开更多
The present paper discusses the modeling of tool geometry effects on the friction stir aluminum welds using response surface methodology. The friction stir welding tools were designed with different shoulder and tool ...The present paper discusses the modeling of tool geometry effects on the friction stir aluminum welds using response surface methodology. The friction stir welding tools were designed with different shoulder and tool probe geometries based on a design matrix. The matrix for the tool designing was made for three types of tools, based on three types of probes, with three levels each for defining the shoulder surface type and probe profile geometries. Then, the effects of tool shoulder and probe geometries on friction stirred aluminum welds were experimentally investigated with respect to weld strength, weld cross section area, grain size of weld and grain size of thermo-mechanically affected zone. These effects were modeled using multiple and response surface regression analysis. The response surface regression modeling were found to be appropriate for defining the friction stir weldment characteristics.展开更多
The analysis of numerous experimental equations published in the literature reveals awide scatter in the predictions for the static recrystallization kinetics of steels. Thepowers of the deformation variables, strain ...The analysis of numerous experimental equations published in the literature reveals awide scatter in the predictions for the static recrystallization kinetics of steels. Thepowers of the deformation variables, strain and strain rate, similarly as the powerof the grain size vary in these equations. These differences are highlighted and thetypical values are compared between torsion and compression tests. Potential errorsin physical simulation testing are discussed.展开更多
Protective hardware is essential for mitigating damage caused by unavoidable falls in humanoid robots.Despite notable progress in fall protection hardware,the theoretical foundation for modeling and the feasibility of...Protective hardware is essential for mitigating damage caused by unavoidable falls in humanoid robots.Despite notable progress in fall protection hardware,the theoretical foundation for modeling and the feasibility of conducting full-scale fall experiments on robots or their surrogates remain somewhat limited.This paper proposes a method for optimizing the thickness of Expandable Polyethylene(EPE),which is used as back protection for the Chubao humanoid robot,based on small-scale impact test data to predict full-scale behavior.The optimal thickness is defined as a balance between compact design and protective effectiveness.An equivalent impact model characterized by four parameters:contact area S,mass m,fall height h,and cushioning material thickness d is introduced to describe impact conditions.The relationship between the peak impact acceleration ap and material thickness d,which forms the core of the method and gives rise to the name AP-D,is analyzed through their plotted curves.After introducing three characteristic parameters and two correction fac-tors,the relationship among the aforementioned variables is derived.Subsequently,both the optimal thickness do and its corresponding peak impact acceleration aop are predicted via nonlinear and linear regression models.Finally,the accuracy and effectiveness of the theoretically derived optimal thickness are validated on both a dummy and the actual robot.With the cushioning material applied,the peak chest acceleration is reduced to 41.57g for the dummy and 32.08g for the robot.展开更多
There is a lack of studies when dealing with the comparison between regression methods and machine learning(ML)-type methods in terms of their ability to interpret and describe how the components of a bituminous mixtu...There is a lack of studies when dealing with the comparison between regression methods and machine learning(ML)-type methods in terms of their ability to interpret and describe how the components of a bituminous mixture affect mechanistic performance.At the same time,artificial intelligence(AI)-driven approaches are becoming more popular in analysing asphalt mixtures,yet there are limited comparisons of regression and machine learning(ML)models for mechanistic performance interpretation.Consequently,a comparison of AI and statistical approaches is presented in this study for predicting bituminous mixture properties such as stiffness,fatigue resistance,and tensile strength.Some of the important input features are bitumen content,crumb rubber content,and air void content.The research uses random forest model(RFM),linear regression model(LRM),and polynomial regression model(PRM).RFM and PRM achieved an R^(2) as high as 0.94,with mean absolute error(MAE)less than 2.5,and are,therefore,good predictive models.Interestingly,RFM works best in one-third of instances,particularly when dealing with outliers,whereas traditional statistical models work better in two-thirds of instances.The results highlight AI's value in bituminous mixture optimisation,where RFM showed good prediction accuracy.In 30%of the cases,AI models outperformed the conventional statistical approaches.At the same time,analyses show that model performance varies significantly with scenarios and that even if AI models capture complex nonlinear relationships,they must not override DOE principles.展开更多
This work addresses optimality aspects related to composite laminates having layers with different orientations.RegressionNeuralNetworks can model the mechanical behavior of these laminates,specifically the stressstra...This work addresses optimality aspects related to composite laminates having layers with different orientations.RegressionNeuralNetworks can model the mechanical behavior of these laminates,specifically the stressstrain relationship.If this model has strong generalization ability,it can be coupled with a metaheuristic algorithm–the PSO algorithm used in this article–to address an optimization problem(OP)related to the orientations of composite laminates.To solve OPs,this paper proposes an optimization framework(OFW)that connects the two components,the optimal solution search mechanism and the RNN model.The OFW has two modules:the search mechanism(Adaptive Hybrid Topology PSO)and the Prediction and Computation Module(PCM).The PCM undertakes all the activities concerning the OP at hand:the stress-strain model,constraints checking,and computation of the objective function.Two case studies about the layers’orientations of laminated specimens are conducted to validate the proposed framework.The specimens belong to“Off-axis oriented specimens”and are subjects of two OPs.The algorithms for AHTPSO and for the two PCMs(one for each problem)are proposed and implemented by MATLAB scripts and functions.Simulations are carried out for different initial conditions.The solutions demonstrated that the OFW is effective and has a highly acceptable computational complexity.The limitation of using the OFWis the generalization ability of the RNN model or any other regression models.To harness the RNN model efficiently,it must have a very good generalization power.If this condition ismet,the OFWcan be integrated into any design process to make optimal choices of the layers’orientations.展开更多
文摘From economy to political administrations, education to health, environment to human rights, many problems we met have gained a global importance in recent days. Existing state systems, political parties and nation states are not adequate for solving these problems in question effectively on their own. Not only governments and local authorities but also voluntary organizations based on completely voluntary activities have significant roles in solving these problems. Effective performance of voluntary organizations depends on increasing volunteer population. Individuals＇ attitudes or their perception of understanding volunteerism play an important role in their contributions to voluntary organizations. The aim of this study is to determine individuals＇ ways of perceiving volunteerism concept and their tendency towards it. Furthermore, differences between men and women＇s perception and attitudes towards volunteerism concept have been examined. For this purpose, a survey has been conducted over university students of bachelor＇s degree. Tendencies and attitudes towards volunteerism compared to gender differences have been tested via logistic regression method. Research results reveal that women take part in voluntary activities more than men and women perceive volunteerism as ＂a political position＂ while men perceive volunteerism as ＂a learning atmosphere and learning process＂.
文摘A complex terrain and topography resulted in an enormous landslide-dammed area northeast of Afghanistan. Moreover, debris, rock avalanches, and landslides occurrences are the primary source of lakes created within the area. Recently, instances have increased because of the high displacement and mass movement by glacial and seismic activities. In this study, using GIS and R statistical software, we performed a logistic regression modeling in order to map and predict the probability of landslides-dammed occurrences. Totally, 361 lakes were mapped using Google Earth historical imagery. This total was divided into 253 （70%） lakes for modeling and 801 （30%） lakes for the model validation. They were randomly selected by creating a fishnet for the study area using Arc toolbox in GIS. Four independent variables that are mostly contributed to landslide-dammed occurrences consisting of slope angles, relief classes, distances to major water sources and earthquake epicenters, were extracted from DEM （digital elevation model） data using 85-meter resolution. The result is a grid map that classified the area into Low （16,834.98 km2）, Medium （2,217.302 kin：） and High （2,013.55 km2） vulnerability to landslide-dammed occurrences. Overall, the model result has been validated by using a ROC （receiver operator characteristic） curve available in SPSS software. The model validation showed a 95.1 percent prediction accuracy that is considered satisfactory.
基金supported by the Health and Medical Research Fund(20190572)the Food and Health Bureau,The Government of the Hong Kong Special Administrative Region,General Research Fund(17122322 and 17126919)+9 种基金the Research Grants Council of the Hong Kong Special Administrative Region Government,NSFC Projects(T2122002,22077079,81871448)Ministry of Science and Technology of China Project(2022YFC2601700,2022YFF0710202)Shanghai Municipal Science and Technology Project(22Z510202478)Shanghai Jiao Tong University Projects(YG2021ZD19)Shanghai Municipal Health Commission Project(2019CXJQ03)Sanming Project of Medicine in Shenzhen,China(SZSM201911014)the High Level-Hospital Program,Health Commission of Guangdong Province,Chinathe research project of Hainan Academician Innovation Platform(YSPTZX202004)the University of Hong Kong Outstanding Young Researcher Awardand the University of Hong Kong Research Output Prize(Li Ka Shing Faculty of Medicine).
文摘Combinatorial drug therapies are generally more effective than monotherapies in treating viral infections.However,it is critical for dose optimization to maximize the efficacy and minimize side effects.Although various strategies have been devised to accelerate the optimization process,their efficiencies were limited by the high noises and suboptimal reproducibility of biological assays.With conventional methods,variances among the replications are used to evaluate the errors of the readouts alone rather than actively participating in the optimization.Herein,we present the Regression Modeling Enabled by Monte Carlo Method(ReMEMC)algorithm for rapid identification of effective combinational therapies.ReMEMC transforms the sample variations into probability distributions of the regression coefficients and predictions.In silico simulations revealed that ReMEMC outperformed conventional regression methods in benchmark problems,and demonstrated its superior robustness against experimental noises.Using COVID-19 as a model disease,ReMEMC successfully identified an optimal 3-drug combination among 10 anti-SARS-CoV-2 drug compounds within two rounds of experiments.The optimal combination showed 2-log and 3-log higher load reduction than non-optimized combinations and monotherapy,respectively.Further workflow refinement allowed identification of personalized drug combinational therapies within 5 days.The strategy may serve as an efficient and universal tool for dose combination optimization.
基金supported by Science,Technology and Innovation Commission of Shenzhen Municipality,China under grant SGDX20210823103535009by the National Natural Science Foundation of China under grants 12172109 and 12302293+1 种基金by the Guangdong Basic and Applied Basic Research Foundation,China under grant 2022A1515011492by the Shenzhen Science and Technology Program,China der un-grants JCYJ20220531095605012,KJZD20230923115210021 and 29853MKCJ202300205.
文摘Data-driven regression models are generally calibrated by minimizing a representation error.However,opti-mizing the model accuracy may create nonphysical wiggles.In this study,we propose topological consistency as a new metric to mitigate these wiggles.The key enabler is Persistent Data Topology(PDT)which extracts a topological skeleton from discrete scalar field data.PDT identifies the extrema of the model based on a neighborhood analysis.The topological error is defined as the mismatch of extrema between the data and the model.The methodology is exemplified for the modeling of the Laminar Burning Velocity(LBV)of ammonia-hydrogen flames.Four regression models,Multi-layer Perceptron(MLP),eXtreme Gradient Boosting(XGBoost),Random Forest(RF),and Light Gradient Boosting Machine(Light GBM),are trained using the data generated by a modified GRI3.0 mechanism.In comparison,MLP builds a model that achieves the highest accuracy and preserves the topological structure of the data.We expect that the proposed topologically consistent regression modeling will enjoy many more applications in model calibration,model selection and optimization algorithms.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
基金Supported by High-level Professional Groups in Gangdong Province,No.GSPZYQ2020101Guangdong Province Educational Research Planning Project,No.2024GXJK742。
文摘BACKGROUND Paternal perinatal depression(PPD)is closely associated with maternal mental health challenges,marital strain,and adverse child developmental outcomes.Despite its significant impact,PPD remains under-recognized in family-centered clinical practice.Concurrently,against the backdrop of rising rates of delayed marriage and China’s Maternity Incentive Policy,the proportion of women giving birth at an advanced maternal age is increasing.Nevertheless,research specifically examining PPD among spouses of older mothers remains critically scarce,both in China and globally.AIM To investigate PPD and its influencing factors in Chinese advanced maternal age families.METHODS This cross-sectional study included 358 participants;it was conducted among fathers of pregnant women of advanced maternal age at five hospitals in the Pearl River Delta region of China from September 2023 to June 2024.Data were collected via a general information questionnaire,the Social Support Rating Scale,and the Edinburgh Postnatal Depression Scale.Latent profile analysis and regression mixture models(RMMs)were adopted to analyze the latent PPD types and factors that influenced PPD.RESULTS The incidence of PPD was 16.48%,and three profiles were identified:Low-symptomatic(175 cases,48.89%),monophasic(140 cases,39.10%),and high-symptomatic(43 cases,12.01%).The RMM analysis revealed that first pregnancy,low income(<¥3000/month),part-time work,and a history of abnormal pregnancy were positively associated with the high-symptomatic type(P<0.05).Conversely,high subjective support and support utilization were negatively associated with the high-symptomatic type compared with the low-symptomatic type(P<0.05).Good couple relationships,high objective and subjective support,and high support utilization were negatively associated with monophasic disorder(P<0.05).CONCLUSION PPD incidence is high among Chinese fathers with advanced maternal age partners,and the characteristics of depression are varied.Healthcare practitioners should prioritize individuals with low levels of social support.
文摘The energy sector in Poland is the source of 81% of greenhouse gas（GHG） emissions. Poland,among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO2 emissions demonstrate a high relationship（0.97） with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH4 emission, indicated the presence of hard coal（0.66）, peat and fuel wood（0.34）, solid waste fuels, as well as other sources（- 0.64） as the most important variables. The adjusted coefficient is suitable and equals R2= 0.90. For N2 O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N2 O emission is the peat and wood fuel consumption.
基金financed as part of the project“Development of a methodology for instrumental base formation for analysis and modeling of the spatial socio-economic development of systems based on internal reserves in the context of digitalization”(FSEG-2023-0008)funded by the Russian Science Foundation(Agreement 23-41-10001,https://doi.org/https://rscf.ru/project/23-41-10001/).
文摘The results of mass appraisal in many countries are used as a basis for calculating the amount of real estate tax,therefore,regardless of the methods used to calculate it,the resulting value should be as close as possible to the market value of the real estate to maintain a balance of interests between the state and the rights holders.In practice,this condition is not always met,since,firstly,the quality of market data is often very low,and secondly,some markets are characterized by low activity,which is expressed in a deficit of information on asking prices.The aim of the work is ecological valuation of land use:how regression-based mass appraisal can inform ecological conservation,land degradation,and sustainable land management.Four multiple regression models were constructed for AI generated map of land plots for recreational use in St.Petersburg(Russia)with different volumes of market information(32,30,20 and 15 units of market information with four price-forming factors).During the analysis of the quality of the models,it was revealed that the best result is shown by the model built on the maximum sample size,then the model based on 15 analogs,which proves that a larger number of analog objects does not always allow us to achieve better results,since the more analog objects there are.
基金Under the auspices of National Natural Science Foundation of China(No.40601073,41101192,41201571)Fundamental Research Funds for the Central Universities(No.2011PY112,2011QC041,2011QC091)Huazhong Agricultural University Scientific&Technological Self-innovation Foundation(No.2011SC21)
文摘This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group(No.RG-1440-022).
文摘The soil water status was investigated under soil surface mulching techniques and two drip line depths from the soil surface(DL).These techniques were black plastic film(BPF),palm tree waste(PTW),and no mulching(NM)as the control treatment.The DL were 15 cm and 25 cm,with surface drip irrigation used as the control.The results indicated that both the BPF and PTW mulching enhanced the soil water retention capacity and there was about 6%water saving in subsurface drip irrigation,compared with NM.Furthermore,the water savings at a DL of 25 cm were lower(15-20 mm)than those at a DL of 15 cm(19-24 mm),whereas surface drip irrigation consumed more water.The distribution of soil water content(θv)for BPF and PTW were more useful than for NM.Hence,mulching the soil with PTW is recommended due to the lower costs and using a DL of 15 cm.Theθv values were derived using multiple linear regression(MLR)and multiple nonlinear regression(MNLR)models.Multiple regression analysis revealed the superiority of the MLR over the MNLR model,which in the training and testing processes had coefficients of correlation of 0.86 and 0.88,root mean square errors of 0.37 and 0.35,and indices of agreement of 0.99 and 0.93,respectively,over the MNLR model.Moreover,DL and spacing from the drip line had a significant effect on the estimation of θv.
文摘Cyber losses in terms of number of records breached under cyber incidents commonly feature a significant portion of zeros, specific characteristics of mid-range losses and large losses, which make it hard to model the whole range of the losses using a standard loss distribution. We tackle this modeling problem by proposing a three-component spliced regression model that can simultaneously model zeros, moderate and large losses and consider heterogeneous effects in mixture components. To apply our proposed model to Privacy Right Clearinghouse (PRC) data breach chronology, we segment geographical groups using unsupervised cluster analysis, and utilize a covariate-dependent probability to model zero losses, finite mixture distributions for moderate body and an extreme value distribution for large losses capturing the heavy-tailed nature of the loss data. Parameters and coefficients are estimated using the Expectation-Maximization (EM) algorithm. Combining with our frequency model (generalized linear mixed model) for data breaches, aggregate loss distributions are investigated and applications on cyber insurance pricing and risk management are discussed.
基金supported in part by Sichuan Science and Technology Program under Grant No.2025ZNSFSC151in part by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDA27030201+1 种基金the Natural Science Foundation of China under Grant No.U21B6001in part by the Natural Science Foundation of Tianjin under Grant No.24JCQNJC01930.
文摘The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example.
基金The National Natural Science Foundation of China(No.51106025,51106027,51036002)Specialized Research Fund for the Doctoral Program of Higher Education(No.20130092110061)the Youth Foundation of Nanjing Institute of Technology(No.QKJA201303)
文摘A fuzzy observations-based radial basis function neural network （FORBFNN） is presented for modeling nonlinear systems in which the observations of response are imprecise but can be represented as fuzzy membership functions. In the FORBFNN model, the weight coefficients of nodes in the hidden layer are identified by using the fuzzy expectation-maximization （ EM ） algorithm, whereas the optimal number of these nodes as well as the centers and widths of radial basis functions are automatically constructed by using a data-driven method. Namely, the method starts with an initial node, and then a new node is added in a hidden layer according to some rules. This procedure is not terminated until the model meets the preset requirements. The method considers both the accuracy and complexity of the model. Numerical simulation results show that the modeling method is effective, and the established model has high prediction accuracy.
文摘Under-fitting problems usually occur in regression models for dam safety monitoring.To overcome the local convergence of the regression, a genetic algorithm (GA) was proposed using a real parameter coding, a ranking selection operator, an arithmetical crossover operator and a uniform mutation operator, and calculated the least-square error of the observed and computed values as its fitness function. The elitist strategy was used to improve the speed of the convergence. After that, the modified genetic algorithm was applied to reassess the coefficients of the regression model and a genetic regression model was set up. As an example, a slotted gravity dam in the Northeast of China was introduced. The computational results show that the genetic regression model can solve the under-fitting problems perfectly.
文摘Because of the relativity among the parameters, partial least square regression(PLSR)was applied to build the model and get the regression equation. The improved algorithm simplified the calculating process greatly because of the reduction of calculation. The orthogonal design was adopted in this experiment. Every sample had strong representation, which could reduce the experimental time and obtain the overall test data. Combined with the formation problem of gas metal arc weld with big current, the auxiliary analysis technique of PLSR was discussed and the regression equation of form factors (i.e. surface width, weld penetration and weld reinforcement) to process parameters(i.e. wire feed rate, wire extension, welding speed, gas flow, welding voltage and welding current)was given. The correlativity structure among variables was analyzed and there was certain correlation between independent variables matrix X and dependent variables matrix Y. The regression analysis shows that the welding speed mainly influences the weld formation while the variation of gas flow in certain range has little influence on formation of weld. The fitting plot of regression accuracy is given. The fitting quality of regression equation is basically satisfactory.
基金supported by the Department of Scientific and Industrial Research(DSIR),India
文摘The present paper discusses the modeling of tool geometry effects on the friction stir aluminum welds using response surface methodology. The friction stir welding tools were designed with different shoulder and tool probe geometries based on a design matrix. The matrix for the tool designing was made for three types of tools, based on three types of probes, with three levels each for defining the shoulder surface type and probe profile geometries. Then, the effects of tool shoulder and probe geometries on friction stirred aluminum welds were experimentally investigated with respect to weld strength, weld cross section area, grain size of weld and grain size of thermo-mechanically affected zone. These effects were modeled using multiple and response surface regression analysis. The response surface regression modeling were found to be appropriate for defining the friction stir weldment characteristics.
文摘The analysis of numerous experimental equations published in the literature reveals awide scatter in the predictions for the static recrystallization kinetics of steels. Thepowers of the deformation variables, strain and strain rate, similarly as the powerof the grain size vary in these equations. These differences are highlighted and thetypical values are compared between torsion and compression tests. Potential errorsin physical simulation testing are discussed.
基金Natural Science Foundation of Beijing Municipality under Grant L243004the National Natural Science Foundation of China under Grant 62403060.
文摘Protective hardware is essential for mitigating damage caused by unavoidable falls in humanoid robots.Despite notable progress in fall protection hardware,the theoretical foundation for modeling and the feasibility of conducting full-scale fall experiments on robots or their surrogates remain somewhat limited.This paper proposes a method for optimizing the thickness of Expandable Polyethylene(EPE),which is used as back protection for the Chubao humanoid robot,based on small-scale impact test data to predict full-scale behavior.The optimal thickness is defined as a balance between compact design and protective effectiveness.An equivalent impact model characterized by four parameters:contact area S,mass m,fall height h,and cushioning material thickness d is introduced to describe impact conditions.The relationship between the peak impact acceleration ap and material thickness d,which forms the core of the method and gives rise to the name AP-D,is analyzed through their plotted curves.After introducing three characteristic parameters and two correction fac-tors,the relationship among the aforementioned variables is derived.Subsequently,both the optimal thickness do and its corresponding peak impact acceleration aop are predicted via nonlinear and linear regression models.Finally,the accuracy and effectiveness of the theoretically derived optimal thickness are validated on both a dummy and the actual robot.With the cushioning material applied,the peak chest acceleration is reduced to 41.57g for the dummy and 32.08g for the robot.
基金sustained them with this research(including Eng.Giuseppe Colicchio)and the European Commission for its financial contribution to the LIFE SILENT project“Sustainable Innovations for Long-life Environmental Noise Technologies”(LIFE22-ENV-IT-LIFE-SILENT/101114310.Acronym:LIFE22-ENV-ITLIFE SILENT)the LIFE SNEAK Project“Optimised Surfaces Against Noise and Vibrations Produced by Tramway Track and Road Traffic”(LIFE20 ENV/IT/000181.Acronym:LIFE SNEAK).
文摘There is a lack of studies when dealing with the comparison between regression methods and machine learning(ML)-type methods in terms of their ability to interpret and describe how the components of a bituminous mixture affect mechanistic performance.At the same time,artificial intelligence(AI)-driven approaches are becoming more popular in analysing asphalt mixtures,yet there are limited comparisons of regression and machine learning(ML)models for mechanistic performance interpretation.Consequently,a comparison of AI and statistical approaches is presented in this study for predicting bituminous mixture properties such as stiffness,fatigue resistance,and tensile strength.Some of the important input features are bitumen content,crumb rubber content,and air void content.The research uses random forest model(RFM),linear regression model(LRM),and polynomial regression model(PRM).RFM and PRM achieved an R^(2) as high as 0.94,with mean absolute error(MAE)less than 2.5,and are,therefore,good predictive models.Interestingly,RFM works best in one-third of instances,particularly when dealing with outliers,whereas traditional statistical models work better in two-thirds of instances.The results highlight AI's value in bituminous mixture optimisation,where RFM showed good prediction accuracy.In 30%of the cases,AI models outperformed the conventional statistical approaches.At the same time,analyses show that model performance varies significantly with scenarios and that even if AI models capture complex nonlinear relationships,they must not override DOE principles.
基金supported by the Ministry of Research,Innovation and Digitization,CNCS/CCCDI–UEFISCDI(Romania),Nr.11/2024,within PNCDI IV.The APC received no external funding.
文摘This work addresses optimality aspects related to composite laminates having layers with different orientations.RegressionNeuralNetworks can model the mechanical behavior of these laminates,specifically the stressstrain relationship.If this model has strong generalization ability,it can be coupled with a metaheuristic algorithm–the PSO algorithm used in this article–to address an optimization problem(OP)related to the orientations of composite laminates.To solve OPs,this paper proposes an optimization framework(OFW)that connects the two components,the optimal solution search mechanism and the RNN model.The OFW has two modules:the search mechanism(Adaptive Hybrid Topology PSO)and the Prediction and Computation Module(PCM).The PCM undertakes all the activities concerning the OP at hand:the stress-strain model,constraints checking,and computation of the objective function.Two case studies about the layers’orientations of laminated specimens are conducted to validate the proposed framework.The specimens belong to“Off-axis oriented specimens”and are subjects of two OPs.The algorithms for AHTPSO and for the two PCMs(one for each problem)are proposed and implemented by MATLAB scripts and functions.Simulations are carried out for different initial conditions.The solutions demonstrated that the OFW is effective and has a highly acceptable computational complexity.The limitation of using the OFWis the generalization ability of the RNN model or any other regression models.To harness the RNN model efficiently,it must have a very good generalization power.If this condition ismet,the OFWcan be integrated into any design process to make optimal choices of the layers’orientations.
