Drowsy driving has received comparatively less attention in traffic safety literature when compared to other safety issues,despite its devastating impact on society in terms of human life lost and associated economic ...Drowsy driving has received comparatively less attention in traffic safety literature when compared to other safety issues,despite its devastating impact on society in terms of human life lost and associated economic burdens.Therefore,this significant safety threat requires a thorough investigation.To address the temporal instability of factors contributing to crashes involving drowsy drivers,this paper divides the crash data into four time periods while capturing unobserved heterogeneity in the means and variances of random parameters.To explore the determinants affecting the severity of injuries sustained by drowsy drivers involved in single-vehicle crashes,injury outcomes are categorized into three groups:serious,moderate,and no injuries.Using four years of crash data from the state of Washington between 2013 and 2016,a wide range of factors were examined,including driver characteristics,roadway conditions,crash characteristics,vehicle conditions,lighting conditions,and temporal factors.The estimation results reveal that there is temporal instability in terms of the effect of determinants on injury severity across the years.However,some factors exhibit stable effects,such as female drivers,sober drivers,and non-hit-and-run crashes.Based on the findings of this study,decision-makers,traffic engineers,and traffic authorities can gain valuable knowledge and insights into the factors contributing to drowsy-related crashes,enabling them to make informed recommendations for safety countermeasures.展开更多
Estimation of treatment effects is one of the crucial mainstays in economics and sociology studies.The problem will become more serious and complicated if the treatment variable is endogenous for the presence of unobs...Estimation of treatment effects is one of the crucial mainstays in economics and sociology studies.The problem will become more serious and complicated if the treatment variable is endogenous for the presence of unobserved confounding.The estimation and conclusion are likely to be biased and misleading if the endogeny of treatment variable is ignored.In this article,we propose the pseudo maximum likelihood method to estimate treatment effects in nonlinear models.The proposed method allows the unobserved confounding and random error terms to exist in an arbitrary relationship(such as,add or multiply),and the unobserved confounding have different influence directions on treatment variables and outcome variables.The proposed estimator is consistent and asymptotically normally distributed.Simulation studies show that the proposed estimator performs better than the special regression estimator,and the proposed method is stable for various distribution of error terms.Finally,the proposed method is applied to the real data that studies the influence of individuals have health insurance on an individual’s decision to visit a doctor.展开更多
Consider an observed binary regressor D and an unobserved binary vari- able D＊, both of which affect some other variable Y. This paper considers nonpara- metric identification and estimation of the effect of D on Y, ...Consider an observed binary regressor D and an unobserved binary vari- able D＊, both of which affect some other variable Y. This paper considers nonpara- metric identification and estimation of the effect of D on Y, conditioning on D＊ = 0. For example, suppose Y is a person＇s wage, the unobserved D＊ indicates if the person has been to college, and the observed D indicates whether the individual claims to have been to college. This paper then identifies and estimates the difference in av- erage wages between those who falsely claim college experience versus those who tell the truth about not having college. We estimate this average effect of lying to be about 6% to 20%. Nonparametric identification without observing D＊ is obtained ei- ther by observing a variable V that is roughly analogous to an instrument for ordinary measurement error, or by imposing restrictions on model error moments.展开更多
Survival analysis is a fundamental tool in medical science for time-to-event data. However, its application to colony organisms like bees poses challenges due to their social nature. Traditional survival models may no...Survival analysis is a fundamental tool in medical science for time-to-event data. However, its application to colony organisms like bees poses challenges due to their social nature. Traditional survival models may not accurately capture the interdependence among individuals within a colony. Frailty models, accounting for shared risks within groups, offer a promising alternative. This study evaluates the performance of semi-parametric shared frailty models (gamma, inverse normal, and positive stable-in comparison to the traditional Cox model using bees’ survival data). We examined the effect of misspecification of the frailty distribution on regression and heterogeneity parameters using simulation and concluded that the heterogeneity parameter was more sensitive to misspecification of the frailty distribution and choice of initial parameters (cluster size and true heterogeneity parameter) compared to the regression parameter. From the data, parameter estimates for covariates were close for the four models but slightly higher for the Cox model. The shared gamma frailty model provided a better fit to the data in comparison with the other models. Therefore, when focusing on regression parameters, the gamma frailty model is recommended. This research underscores the importance of tailored survival methodologies for accurately analyzing time-to-event data in social organisms.展开更多
Despite increasing knowledge of barnyardgrass(Echinochloa crus-galli) interference with rice, relatively little is known how endophytes improve the ability of rice against barnyardgrass stress. Here, we provided a det...Despite increasing knowledge of barnyardgrass(Echinochloa crus-galli) interference with rice, relatively little is known how endophytes improve the ability of rice against barnyardgrass stress. Here, we provided a detailed temporal characterization of rice root-associated microbiomes during co-cultivation with barnyardgrass and a comparison with the microbiomes of weed-free rice plants. Alpha diversity analysis indicated that barnyardgrass had the opposite effects on endophytic bacteria and fungi in rice roots, in terms of the community diversity, richness and coverage at the rice seedling stage. Principal coordinate analysis showed that barnyardgrass had only a minor effect on the community composition of endophytes in rice roots at the rice seedling stage, but showed a significant and maximum interference at the heading stage. Rice recruited many endophytes to resist biotic stress from barnyardgrass, especially for fungi. PICRUSt(phylogenetic investigation of communities by reconstruction of unobserved states) predictive analysis indicated that 23 metabolic pathways of bacteria were overrepresented in rice. In addition, the main trophic mode of fungi was pathotroph according to FUNGuild analysis. A positive correlation between bacteria and fungi in rice roots was found via network analysis. Anaeromyxobacter, Azospira and Pseudolabrys were the vital bacteria, Phaeosphaeria and Funneliformis were the dominant fungi in maintaining the stability of the ecological network. These results provided data and a theoretical basis for the in-depth understanding of what role endophytes play in rice resistance to barnyardgrass stress and will have implications on improving the resistance of rice against biotic stress using root microbiota.展开更多
This study investigates the degree of capital mobility in a panel of 16 Latin American and 4 Caribbean countries during 1960 to 2017 against the backdrop of the Feldstein-Horioka hypothesis by applying recent panel da...This study investigates the degree of capital mobility in a panel of 16 Latin American and 4 Caribbean countries during 1960 to 2017 against the backdrop of the Feldstein-Horioka hypothesis by applying recent panel data techniques.This is the first study on capital mobility in Latin American and Caribbean countries to employ the recently developed panel data procedure of the dynamic common correlated effects modeling technique of Chudik and Pesaran(J Econ 188:393–420,2015)and the error-correction testing of Gengenbach,Urbain,and Westerlund(Panel error correction testing with global stochastic trends,2008,J Appl Econ 31:982–1004,2016).These approaches address the serious panel data econometric issues of crosssection dependence,slope heterogeneity,nonstationarity,and endogeneity in a multifactor error-structure framework.The empirical findings of this study reveal a low average(mean)savings–retention coefficient for the panel as a whole and for most individual countries,as well as indicating a cointegration relationship between saving and investment ratios.The results indicate that there is a relatively high degree of capital mobility in the Latin American and Caribbean countries in the short run,while the long-run solvency condition is maintained,which is due to reduced frictions in goods and services markets causing increase competition.Increased capital mobility in these countries can promote economic growth and hasten the process of globalization by creating a conducive economic environment for FDI in these countries.展开更多
This paper presents an procedure for purifying training data sets （i.e., past occurrences of slope failures） for inverse estimation on unobserved trigger factors of ＂different types of simultaneous slope failures＂...This paper presents an procedure for purifying training data sets （i.e., past occurrences of slope failures） for inverse estimation on unobserved trigger factors of ＂different types of simultaneous slope failures＂. Due to difficulties in pixel-by-pixel observations of trigger factors, as one of the measures, the authors had proposed an inverse analysis algorithm on trigger factors based on SEM （structural equation modeling）. Through a measurement equation, the trigger factor is inversely estimated, and a TFI （trigger factor influence） map can be also produced. As a subsequence subject, a purification procedure of training data set should be constructed to improve the accuracy of TFI map which depends on the representativeness of given training data sets of different types of slope failures. The proposed procedure resamples the matched pixels between original groups of past slope failures （i.e., surface slope failures, deep-seated slope failures, landslides） and classified three groups by K-means clustering for all pixels corresponding to those slope failures. For all cases of three types of slope failures, the improvement of success rates with respect to resampled training data sets was confirmed. As a final outcome, the differences between TFI maps produced by using original and resampled training data sets, respectively, are delineated on a DIF map （difference map） which is useful for analyzing trigger factor influence in terms of ＂risky- and safe-side assessment＂ sub-areas with respect to ＂different types of simultaneous slope failures＂.展开更多
This paper deals with the synthesis of Petri net supervisor enforcing the more expressive constraints including marking terms, firing vector terms and Parikh vector terms. The method is developed to handle uncontrolla...This paper deals with the synthesis of Petri net supervisor enforcing the more expressive constraints including marking terms, firing vector terms and Parikh vector terms. The method is developed to handle uncontrollable and unobservable transitions existing in the constraints. The “greater-than or equal” general constraints can also be transformed into “less-than or equal” Parikh constraints. An example is analyzed to show how the problem is solved. General constraint is first transformed into Parikh vector constraints, and Matrix-Transformation is proposed to obtain the admissible constraints without uncontrollable and unobservable transitions. Then the supervisor can be constructed based on constraints only consisting of Parikh vector terms. The method is proved to be more concise and effective than the method presented by Iordache and Moody especially when applied to large scale systems.展开更多
In this paper,a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net formalism.First,a Petri net for the deadlock control of such syst...In this paper,a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net formalism.First,a Petri net for the deadlock control of such systems is defined.Its admissible markings and first-met inadmissible markings(FIMs)are introduced.Next,place invariants are designed via an integer linear program(ILP)to survive all admissible markings and prohibit all FIMs,keeping the underlying system from reaching deadlocks,livelocks,bad markings,and the markings that may evolve into them by firing uncontrollable transitions.ILP also ensures that the obtained deadlock-free supervisor does not observe any unobservable transition.In addition,the supervisor is guaranteed to be admissible and structurally minimal in terms of both control places and added arcs.The condition under which the supervisor is maximally permissive in behavior is given.Finally,experimental results with the proposed method and existing ones are given to show its effectiveness.展开更多
The observability problem of switched linear singular（SLS） systems is studied in this paper. Based on the observability definition, the unobservable subspaces of given switching laws are investigated under the condi...The observability problem of switched linear singular（SLS） systems is studied in this paper. Based on the observability definition, the unobservable subspaces of given switching laws are investigated under the condition that all subsystems are regular. A necessary condition and a sufficient condition for observability of SLS systems are given. It is shown that the observability and controllability are dual for some special SLS systems with circulatory switching laws. The method developed here is applicable to the observability analysis of normal switched linear systems.展开更多
This paper mainly discusses the singular linear systems of distributional version. By using its matrices coefficients and some invariant subspaces, the distributionally weakly obseervability and the impulse observabil...This paper mainly discusses the singular linear systems of distributional version. By using its matrices coefficients and some invariant subspaces, the distributionally weakly obseervability and the impulse observability are analyzed.展开更多
This paper analyzes the adoption dynamics of improved rainfed maize seeds disseminated in Senegal in 2013 by the West African Agricultural Productivity Program (WAAPP). We group maize producers into five groups (non-a...This paper analyzes the adoption dynamics of improved rainfed maize seeds disseminated in Senegal in 2013 by the West African Agricultural Productivity Program (WAAPP). We group maize producers into five groups (non-adopters, laggards/abandoners, late adopters, followers and pioneers/innovators) and take into account the heterogeneity of unobservable characteristics of the producers. In the pioneers/innovators group, the availability of labour, household size, shocks, and frequency of access to advice positively influence adoption, whereas financial constraints and high numbers of plots reduce the probability of adoption. Producers in the followers’ category tend to be older and more educated than those in the other categories. However, food insecurity and shocks such as diseases hamper adoption. For the group of late adopters, household size and available storage infrastructures explain adoption. However, the number of plots and shocks reduces their probability of adoption. Laggards tend to face shocks and food insecurity. The authors recommend to consider the dynamics of the adoption of technological innovations and heterogeneity of the characteristics of adopters groups in future research. They also recommend farmers to increase their adoption rate of the “Early Thai” and “Suwan 1” seed varieties thanks to their higher yields compared to traditional varieties. Also, a higher adoption rate would positively impact the food security of maize farmers in Eastern Senegal and High Casamance, especially in terms of availability. Other studies measuring the number of years needed for large-scale adoption of improved seed varieties should be conducted.展开更多
This paper investigates a dynamic mean variance investment decision problem with partial information,where the stock return is assumed to consist of an observable factor and an unobservable factor,which both follow me...This paper investigates a dynamic mean variance investment decision problem with partial information,where the stock return is assumed to consist of an observable factor and an unobservable factor,which both follow mean reversion processes.Through the Bayesian learning mechanism,the unobservable components of stock returns can be learned by investors from available information,including stock prices and observable returns.Due to lack of time consistency in dynamic investment decision problem with mean-variance criterion,the authors solve this problem by using a game theory approach and characterize the equilibrium investment strategy through the extended Hamilton-Jacobi-Bellman equation(HJB)equations system.By solving the extended HJB equations system,the semianalytical solutions of the equilibrium strategy and the corresponding value function are obtained.In addition,the influence of unobserved predictor and learning mechanism on the equilibrium investment strategy is also analyzed by utilizing numerical examples.展开更多
This research investigates the intricate relationship between the number of lanes on highways and injury severities sustained by commercial motor vehicle(CMV)drivers.Many studies have addressed crash determinants,but ...This research investigates the intricate relationship between the number of lanes on highways and injury severities sustained by commercial motor vehicle(CMV)drivers.Many studies have addressed crash determinants,but the safety implications of differing numbers of lanes remain insufficiently examined,especially during the highway planning stages.Our study fills this knowledge gap by analyzing injury severity crash factors for a varied number of lane scenarios.Employing a random parameter logit modeling framework,we differentiated injury levels for 2-4 lanes and 6-10 lanes.Key factors were identified for each number of lanes,with older,loss of vehicle control,non-collision crashes,and crashes,on locations where grade or hill existed,being more perilous and increasing the risk of sustaining severe injuries on 2-lane highways.For 4-lane highways,factors such as non-Oregonian drivers,older drivers,crashes that occurred during the spring season,and crashes that occurred beyond shoulders were associated with an elevated probability of being involved in severe injury crashes.Regarding highways with 6 lanes and higher,driving too fast for conditions and driver error(drowsy,fatigued,inattentive,or reckless)increases the odds of being involved in higher levels of injury crashes.To enhance truck driver safety,we recommend the implementation of electronic stability control in CMVs,with moderated speeds on graded sections,improved curve markers,and robust public safety campaigns.展开更多
This paper investigates the problem of cooperative localization(CL)for a multi-robot system(MRS)under dynamic measurement topology,which involves a group of robots collectively estimating their poses with respect to a...This paper investigates the problem of cooperative localization(CL)for a multi-robot system(MRS)under dynamic measurement topology,which involves a group of robots collectively estimating their poses with respect to a common reference frame using ego-motion measurements and robot-to-robot relative measurements.The authors provide a theoretical analysis of the time-varying unobservable subspace and propose a consistent cooperative localization algorithm.First,the authors introduce the relative measurement graph(RMG)to represent the relative pose measurements obtained by the MRS at each instant.Then,the authors derive the local observability matrix over a time interval.An equivalent relationship is established between the local observability matrix and the spectral matrices of the RMG.Moreover,the authors present a method for constructing the unobservable subspace based on the RMG under different topology conditions.Based on this analysis,the authors design a consistent cooperative localization algorithm that satisfies the constraints of the time-varying unobservable subspace.An analytical optimal solution is derived for the constrained optimization problem.Monte Carlo numerical simulations are conducted to demonstrate the consistency and accuracy of the proposed method.展开更多
As the phasor measurement unit(PMU)placement problem involves a cost-benefit trade-off,more PMUs get placed on higher-voltage buses.However,this leads to the fact that many lower-voltage levels of the bulk power syste...As the phasor measurement unit(PMU)placement problem involves a cost-benefit trade-off,more PMUs get placed on higher-voltage buses.However,this leads to the fact that many lower-voltage levels of the bulk power system cannot be observed by PMUs.This lack of visibility then makes timesynchronized state estimation of the full system a challenging problem.In this paper,a deep neural network-based state estimator(DeNSE)is proposed to solve this problem.The DeNSE employs a Bayesian framework to indirectly combine the inferences drawn from slow-timescale but widespread supervisory control and data acquisition(SCADA)data with fast-timescale but selected PMU data,to attain sub-second situational awareness of the full system.The practical utility of the DeNSE is demonstrated by considering topology change,non-Gaussian measurement noise,and detection and correction of bad data.The results obtained using the IEEE 118-bus system demonstrate the superiority of the DeNSE over a pure SCADA state estimator and a PMU-only linear state estimator from a technoeconomic viability perspective.Lastly,the scalability of the DeNSE is proven by estimating the states of a large and realistic 2000-bus synthetic Texas system.展开更多
For the purpose of exploring the factors affecting injury severity of children and adolescents involved in traffic crashes in Greece,disaggregate crash data including 13,431 involving children and adolescents from all...For the purpose of exploring the factors affecting injury severity of children and adolescents involved in traffic crashes in Greece,disaggregate crash data including 13,431 involving children and adolescents from all regions of Greece for the period 2006–2015 were utilized.In order to identify factors affecting injury severity and account for potential unobserved heterogeneity,a series of mixed logit models were utilized.To explore and address potential temporal instability of crash-related risk factors,the likelihood ratio test was applied.Results indicated that night crashes,crashes outside urban areas as well as crashes involving bicycles or powered-two-wheelers are associated with higher injury severity of children and adolescents.Interestingly,crashes involving pedestrians are associated with lower injury severity than head-on collisions and run-off-road collisions with fixed objects.Side and sideswipe crashes also result in lower injury severities.The likelihood ratio test indicated that crash-related factors are instable when comparing the models utilizing data before and after 2010 respectively.This study contributes to the current knowledge in the field,as to the best of our knowledge this is the first study that addresses unobserved heterogeneity when analyzing child and adolescent injury severity.Overall,the findings of this study provide useful insights and could assist in unveiling crash risk factors and prioritize programs and measures to promote road safety of children and adolescents.展开更多
Every year,a substantial number of children sustain injuries and fatalities in motor vehicle crashes in Wyoming.Understanding the factors contributing to child injury is crucial for the development of appropriate miti...Every year,a substantial number of children sustain injuries and fatalities in motor vehicle crashes in Wyoming.Understanding the factors contributing to child injury is crucial for the development of appropriate mitigation measures that aid in alleviating the severity of such injuries.In this study,a hierarchical Bayesian binary logit regression model was developed to investigate the factors that contribute to children’s injuries resulting from crashes while accounting for possible intra-class correlation effects(those of unobserved factors common to children involved in the same crash).A strong correlation among crashes justified the use of the hierarchical Bayesian logit model.As per the modeling results,the children’s ages,safety restraint types,vehicle types,drivers’ages,alcohol/drug involvement,drivers’seat belt use habits,drivers’actions,manners of collision and environmental conditions contributed to child injury risk.The child’s age was found to be inversely related to the risk of injury.Similarly,among safety restraint types,rear-facing car seats and forward-facing car seats were found to reduce injury likelihoods in crashes.When it comes to the drivers’characteristics,the probability of incurring injuries among the child population increased in the presence of young,unbuckled and impaired drivers.Furthermore,improper driving actions,such as running off the road,raised the risk of incurring injuries to children.The findings of this study may be beneficial to authorities regarding developing and implementing road safety programs aimed at ameliorating child injury concerns.展开更多
Improper lane-changing behaviours may result in breakdown of traffic flow and the occurrence of various types of collisions.This study investigates lane-changing behaviours of multiple vehicles and the stimulative eff...Improper lane-changing behaviours may result in breakdown of traffic flow and the occurrence of various types of collisions.This study investigates lane-changing behaviours of multiple vehicles and the stimulative effect on following drivers in a consecutive lanechanging scenario.The microscopic trajectory data from the HighD dataset are used for driving behaviour analysis.Two discretionary lane-changing vehicle groups constitute a consecutive lane-changing scenario,and not only distance-and speed-related factors but also driving behaviours are taken into account to examine the impacts on the utility of following lane-changing vehicles.A random parameters logit model is developed to capture the driver’s psychological heterogeneity in the consecutive lane-changing situation.Furthermore,a lane-changing utility prediction model is established based on three supervised learning algorithms to detect the improper lane-changing decision.Results indicate that 1)the consecutive lane-changing behaviours have a significant negative effect on the following lane-changing vehicles after lane change;2)the stimulative effect exists in a consecutive lane-change situation and its influence is heterogeneous due to different psychological activities of drivers;and 3)the utility prediction model can be used to detect an improper lane-changing decision.展开更多
Bicycling has been actively promoted as a clean and efficient mode of commute.Besides,due to the personal and societal benefits it provides,it has been adopted by many city dwellers for short-distance trips.Despite th...Bicycling has been actively promoted as a clean and efficient mode of commute.Besides,due to the personal and societal benefits it provides,it has been adopted by many city dwellers for short-distance trips.Despite the integral role this active transport mode plays,it is unfortunately associated with a high risk of fatalities in the event of a traffic crash as they are not protected.Many studies have been conducted in several jurisdictions to examine the factors contributing to crashes involving these vulnerable road users.In the case of Louisiana which is currently experiencing increased cases of severe and fatal bicycleinvolved crashes,less attention has been paid to investigating the critical factors influencing bicyclist injury severity outcomes using more detailed data and advanced econometric modeling frameworks to help propose adequate policies to improve the safety of riders.Against this background,this study examined the key contributing factors influencing bicyclist injuries by using more detailed roadway crash data spanning 2010-2016 obtained from the state of Louisiana.The study then applies an advanced random parameter logit modeling with heterogeneity in means and variances to address the unobserved heterogeneity issue associated with traffic crash data.To overcome the imbalanced data issue,three major crash injury levels were used instead of the conventional five crash injury levels.Besides,the data groups classified under each injury level were compared for the final variable selection.The study found that distracted drivers,elderly bicyclists,careless operations,and riding in dark conditions increase the probability of having severe injuries in vehicle-bicyclist crashes.Moreover,the variables for straight-level roadways and city streets decrease the odds of severe injuries.The straight-level roadway may provide better sight distance for both drivers and bicyclists,and complex environments like city streets discourage crashes with severe injuries.展开更多
文摘Drowsy driving has received comparatively less attention in traffic safety literature when compared to other safety issues,despite its devastating impact on society in terms of human life lost and associated economic burdens.Therefore,this significant safety threat requires a thorough investigation.To address the temporal instability of factors contributing to crashes involving drowsy drivers,this paper divides the crash data into four time periods while capturing unobserved heterogeneity in the means and variances of random parameters.To explore the determinants affecting the severity of injuries sustained by drowsy drivers involved in single-vehicle crashes,injury outcomes are categorized into three groups:serious,moderate,and no injuries.Using four years of crash data from the state of Washington between 2013 and 2016,a wide range of factors were examined,including driver characteristics,roadway conditions,crash characteristics,vehicle conditions,lighting conditions,and temporal factors.The estimation results reveal that there is temporal instability in terms of the effect of determinants on injury severity across the years.However,some factors exhibit stable effects,such as female drivers,sober drivers,and non-hit-and-run crashes.Based on the findings of this study,decision-makers,traffic engineers,and traffic authorities can gain valuable knowledge and insights into the factors contributing to drowsy-related crashes,enabling them to make informed recommendations for safety countermeasures.
基金supported by the National Natural Science Foundation of China (Nos.12101545)by the natural science foundation of Inner Mongolia Autonomous Region (2022MS01007)。
文摘Estimation of treatment effects is one of the crucial mainstays in economics and sociology studies.The problem will become more serious and complicated if the treatment variable is endogenous for the presence of unobserved confounding.The estimation and conclusion are likely to be biased and misleading if the endogeny of treatment variable is ignored.In this article,we propose the pseudo maximum likelihood method to estimate treatment effects in nonlinear models.The proposed method allows the unobserved confounding and random error terms to exist in an arbitrary relationship(such as,add or multiply),and the unobserved confounding have different influence directions on treatment variables and outcome variables.The proposed estimator is consistent and asymptotically normally distributed.Simulation studies show that the proposed estimator performs better than the special regression estimator,and the proposed method is stable for various distribution of error terms.Finally,the proposed method is applied to the real data that studies the influence of individuals have health insurance on an individual’s decision to visit a doctor.
文摘Consider an observed binary regressor D and an unobserved binary vari- able D＊, both of which affect some other variable Y. This paper considers nonpara- metric identification and estimation of the effect of D on Y, conditioning on D＊ = 0. For example, suppose Y is a person＇s wage, the unobserved D＊ indicates if the person has been to college, and the observed D indicates whether the individual claims to have been to college. This paper then identifies and estimates the difference in av- erage wages between those who falsely claim college experience versus those who tell the truth about not having college. We estimate this average effect of lying to be about 6% to 20%. Nonparametric identification without observing D＊ is obtained ei- ther by observing a variable V that is roughly analogous to an instrument for ordinary measurement error, or by imposing restrictions on model error moments.
文摘Survival analysis is a fundamental tool in medical science for time-to-event data. However, its application to colony organisms like bees poses challenges due to their social nature. Traditional survival models may not accurately capture the interdependence among individuals within a colony. Frailty models, accounting for shared risks within groups, offer a promising alternative. This study evaluates the performance of semi-parametric shared frailty models (gamma, inverse normal, and positive stable-in comparison to the traditional Cox model using bees’ survival data). We examined the effect of misspecification of the frailty distribution on regression and heterogeneity parameters using simulation and concluded that the heterogeneity parameter was more sensitive to misspecification of the frailty distribution and choice of initial parameters (cluster size and true heterogeneity parameter) compared to the regression parameter. From the data, parameter estimates for covariates were close for the four models but slightly higher for the Cox model. The shared gamma frailty model provided a better fit to the data in comparison with the other models. Therefore, when focusing on regression parameters, the gamma frailty model is recommended. This research underscores the importance of tailored survival methodologies for accurately analyzing time-to-event data in social organisms.
基金supported by the National Natural Science Foundation of China(Grant No.31701803)Changsha Natural Science Foundation,China(Grant No.kq2202336)the Special Project of Hunan Innovative Province Construction,China(Grant No.S2021ZCKPZT0004)。
文摘Despite increasing knowledge of barnyardgrass(Echinochloa crus-galli) interference with rice, relatively little is known how endophytes improve the ability of rice against barnyardgrass stress. Here, we provided a detailed temporal characterization of rice root-associated microbiomes during co-cultivation with barnyardgrass and a comparison with the microbiomes of weed-free rice plants. Alpha diversity analysis indicated that barnyardgrass had the opposite effects on endophytic bacteria and fungi in rice roots, in terms of the community diversity, richness and coverage at the rice seedling stage. Principal coordinate analysis showed that barnyardgrass had only a minor effect on the community composition of endophytes in rice roots at the rice seedling stage, but showed a significant and maximum interference at the heading stage. Rice recruited many endophytes to resist biotic stress from barnyardgrass, especially for fungi. PICRUSt(phylogenetic investigation of communities by reconstruction of unobserved states) predictive analysis indicated that 23 metabolic pathways of bacteria were overrepresented in rice. In addition, the main trophic mode of fungi was pathotroph according to FUNGuild analysis. A positive correlation between bacteria and fungi in rice roots was found via network analysis. Anaeromyxobacter, Azospira and Pseudolabrys were the vital bacteria, Phaeosphaeria and Funneliformis were the dominant fungi in maintaining the stability of the ecological network. These results provided data and a theoretical basis for the in-depth understanding of what role endophytes play in rice resistance to barnyardgrass stress and will have implications on improving the resistance of rice against biotic stress using root microbiota.
文摘This study investigates the degree of capital mobility in a panel of 16 Latin American and 4 Caribbean countries during 1960 to 2017 against the backdrop of the Feldstein-Horioka hypothesis by applying recent panel data techniques.This is the first study on capital mobility in Latin American and Caribbean countries to employ the recently developed panel data procedure of the dynamic common correlated effects modeling technique of Chudik and Pesaran(J Econ 188:393–420,2015)and the error-correction testing of Gengenbach,Urbain,and Westerlund(Panel error correction testing with global stochastic trends,2008,J Appl Econ 31:982–1004,2016).These approaches address the serious panel data econometric issues of crosssection dependence,slope heterogeneity,nonstationarity,and endogeneity in a multifactor error-structure framework.The empirical findings of this study reveal a low average(mean)savings–retention coefficient for the panel as a whole and for most individual countries,as well as indicating a cointegration relationship between saving and investment ratios.The results indicate that there is a relatively high degree of capital mobility in the Latin American and Caribbean countries in the short run,while the long-run solvency condition is maintained,which is due to reduced frictions in goods and services markets causing increase competition.Increased capital mobility in these countries can promote economic growth and hasten the process of globalization by creating a conducive economic environment for FDI in these countries.
文摘This paper presents an procedure for purifying training data sets （i.e., past occurrences of slope failures） for inverse estimation on unobserved trigger factors of ＂different types of simultaneous slope failures＂. Due to difficulties in pixel-by-pixel observations of trigger factors, as one of the measures, the authors had proposed an inverse analysis algorithm on trigger factors based on SEM （structural equation modeling）. Through a measurement equation, the trigger factor is inversely estimated, and a TFI （trigger factor influence） map can be also produced. As a subsequence subject, a purification procedure of training data set should be constructed to improve the accuracy of TFI map which depends on the representativeness of given training data sets of different types of slope failures. The proposed procedure resamples the matched pixels between original groups of past slope failures （i.e., surface slope failures, deep-seated slope failures, landslides） and classified three groups by K-means clustering for all pixels corresponding to those slope failures. For all cases of three types of slope failures, the improvement of success rates with respect to resampled training data sets was confirmed. As a final outcome, the differences between TFI maps produced by using original and resampled training data sets, respectively, are delineated on a DIF map （difference map） which is useful for analyzing trigger factor influence in terms of ＂risky- and safe-side assessment＂ sub-areas with respect to ＂different types of simultaneous slope failures＂.
基金Project supported by the National Natural Science Foundation of China (No. 60504024), Zhejiang Provincial Education Department(No. 20050905), and "151 Talent Project" of Zhejiang Province,China
文摘This paper deals with the synthesis of Petri net supervisor enforcing the more expressive constraints including marking terms, firing vector terms and Parikh vector terms. The method is developed to handle uncontrollable and unobservable transitions existing in the constraints. The “greater-than or equal” general constraints can also be transformed into “less-than or equal” Parikh constraints. An example is analyzed to show how the problem is solved. General constraint is first transformed into Parikh vector constraints, and Matrix-Transformation is proposed to obtain the admissible constraints without uncontrollable and unobservable transitions. Then the supervisor can be constructed based on constraints only consisting of Parikh vector terms. The method is proved to be more concise and effective than the method presented by Iordache and Moody especially when applied to large scale systems.
基金supported by the National Natural Science Foundation of China(61773206)the Natural Science Foundation of Jiangsu Province of China(BK20170131)+1 种基金Jiangsu Overseas Visiting Scholar Program for University Prominent Young&Middle-aged Teachers and Presidents(2019-19)the Deanship of Scientific Research(DSR)at King Abdulaziz University(RG-20-135-38)。
文摘In this paper,a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net formalism.First,a Petri net for the deadlock control of such systems is defined.Its admissible markings and first-met inadmissible markings(FIMs)are introduced.Next,place invariants are designed via an integer linear program(ILP)to survive all admissible markings and prohibit all FIMs,keeping the underlying system from reaching deadlocks,livelocks,bad markings,and the markings that may evolve into them by firing uncontrollable transitions.ILP also ensures that the obtained deadlock-free supervisor does not observe any unobservable transition.In addition,the supervisor is guaranteed to be admissible and structurally minimal in terms of both control places and added arcs.The condition under which the supervisor is maximally permissive in behavior is given.Finally,experimental results with the proposed method and existing ones are given to show its effectiveness.
基金the National Natural Science Foundation of China (No. 90405017, 60274021, 60334040)China Postdoctoral Science Foundation (No.20060400415)the 973 Program of China (No. 2002CB312205)
文摘The observability problem of switched linear singular（SLS） systems is studied in this paper. Based on the observability definition, the unobservable subspaces of given switching laws are investigated under the condition that all subsystems are regular. A necessary condition and a sufficient condition for observability of SLS systems are given. It is shown that the observability and controllability are dual for some special SLS systems with circulatory switching laws. The method developed here is applicable to the observability analysis of normal switched linear systems.
文摘This paper mainly discusses the singular linear systems of distributional version. By using its matrices coefficients and some invariant subspaces, the distributionally weakly obseervability and the impulse observability are analyzed.
文摘This paper analyzes the adoption dynamics of improved rainfed maize seeds disseminated in Senegal in 2013 by the West African Agricultural Productivity Program (WAAPP). We group maize producers into five groups (non-adopters, laggards/abandoners, late adopters, followers and pioneers/innovators) and take into account the heterogeneity of unobservable characteristics of the producers. In the pioneers/innovators group, the availability of labour, household size, shocks, and frequency of access to advice positively influence adoption, whereas financial constraints and high numbers of plots reduce the probability of adoption. Producers in the followers’ category tend to be older and more educated than those in the other categories. However, food insecurity and shocks such as diseases hamper adoption. For the group of late adopters, household size and available storage infrastructures explain adoption. However, the number of plots and shocks reduces their probability of adoption. Laggards tend to face shocks and food insecurity. The authors recommend to consider the dynamics of the adoption of technological innovations and heterogeneity of the characteristics of adopters groups in future research. They also recommend farmers to increase their adoption rate of the “Early Thai” and “Suwan 1” seed varieties thanks to their higher yields compared to traditional varieties. Also, a higher adoption rate would positively impact the food security of maize farmers in Eastern Senegal and High Casamance, especially in terms of availability. Other studies measuring the number of years needed for large-scale adoption of improved seed varieties should be conducted.
基金supported by the National Natural Science Foundation of China under Grant Nos.71932002,72071051,71871071the General Projects of Social Science Program of Beijing Municipal Commission of Education under Grant No.SM202010005005+1 种基金the General Program of Guangdong Basic and Applied Basic Research Foundation under Grant No.2023A1515011354the Joint Research Project of Teachers and Students of Guangdong University of Foreign Studies under Grant No.21SS11。
文摘This paper investigates a dynamic mean variance investment decision problem with partial information,where the stock return is assumed to consist of an observable factor and an unobservable factor,which both follow mean reversion processes.Through the Bayesian learning mechanism,the unobservable components of stock returns can be learned by investors from available information,including stock prices and observable returns.Due to lack of time consistency in dynamic investment decision problem with mean-variance criterion,the authors solve this problem by using a game theory approach and characterize the equilibrium investment strategy through the extended Hamilton-Jacobi-Bellman equation(HJB)equations system.By solving the extended HJB equations system,the semianalytical solutions of the equilibrium strategy and the corresponding value function are obtained.In addition,the influence of unobserved predictor and learning mechanism on the equilibrium investment strategy is also analyzed by utilizing numerical examples.
文摘This research investigates the intricate relationship between the number of lanes on highways and injury severities sustained by commercial motor vehicle(CMV)drivers.Many studies have addressed crash determinants,but the safety implications of differing numbers of lanes remain insufficiently examined,especially during the highway planning stages.Our study fills this knowledge gap by analyzing injury severity crash factors for a varied number of lane scenarios.Employing a random parameter logit modeling framework,we differentiated injury levels for 2-4 lanes and 6-10 lanes.Key factors were identified for each number of lanes,with older,loss of vehicle control,non-collision crashes,and crashes,on locations where grade or hill existed,being more perilous and increasing the risk of sustaining severe injuries on 2-lane highways.For 4-lane highways,factors such as non-Oregonian drivers,older drivers,crashes that occurred during the spring season,and crashes that occurred beyond shoulders were associated with an elevated probability of being involved in severe injury crashes.Regarding highways with 6 lanes and higher,driving too fast for conditions and driver error(drowsy,fatigued,inattentive,or reckless)increases the odds of being involved in higher levels of injury crashes.To enhance truck driver safety,we recommend the implementation of electronic stability control in CMVs,with moderated speeds on graded sections,improved curve markers,and robust public safety campaigns.
文摘This paper investigates the problem of cooperative localization(CL)for a multi-robot system(MRS)under dynamic measurement topology,which involves a group of robots collectively estimating their poses with respect to a common reference frame using ego-motion measurements and robot-to-robot relative measurements.The authors provide a theoretical analysis of the time-varying unobservable subspace and propose a consistent cooperative localization algorithm.First,the authors introduce the relative measurement graph(RMG)to represent the relative pose measurements obtained by the MRS at each instant.Then,the authors derive the local observability matrix over a time interval.An equivalent relationship is established between the local observability matrix and the spectral matrices of the RMG.Moreover,the authors present a method for constructing the unobservable subspace based on the RMG under different topology conditions.Based on this analysis,the authors design a consistent cooperative localization algorithm that satisfies the constraints of the time-varying unobservable subspace.An analytical optimal solution is derived for the constrained optimization problem.Monte Carlo numerical simulations are conducted to demonstrate the consistency and accuracy of the proposed method.
基金This work was supported in part by the U.S.Department of Energy(No.DEEE0009355)the National Science Foundation(NSF)(No.ECCS-2145063)the Electric Power Research Institute(EPRI)(No.10013085)。
文摘As the phasor measurement unit(PMU)placement problem involves a cost-benefit trade-off,more PMUs get placed on higher-voltage buses.However,this leads to the fact that many lower-voltage levels of the bulk power system cannot be observed by PMUs.This lack of visibility then makes timesynchronized state estimation of the full system a challenging problem.In this paper,a deep neural network-based state estimator(DeNSE)is proposed to solve this problem.The DeNSE employs a Bayesian framework to indirectly combine the inferences drawn from slow-timescale but widespread supervisory control and data acquisition(SCADA)data with fast-timescale but selected PMU data,to attain sub-second situational awareness of the full system.The practical utility of the DeNSE is demonstrated by considering topology change,non-Gaussian measurement noise,and detection and correction of bad data.The results obtained using the IEEE 118-bus system demonstrate the superiority of the DeNSE over a pure SCADA state estimator and a PMU-only linear state estimator from a technoeconomic viability perspective.Lastly,the scalability of the DeNSE is proven by estimating the states of a large and realistic 2000-bus synthetic Texas system.
基金funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 754462
文摘For the purpose of exploring the factors affecting injury severity of children and adolescents involved in traffic crashes in Greece,disaggregate crash data including 13,431 involving children and adolescents from all regions of Greece for the period 2006–2015 were utilized.In order to identify factors affecting injury severity and account for potential unobserved heterogeneity,a series of mixed logit models were utilized.To explore and address potential temporal instability of crash-related risk factors,the likelihood ratio test was applied.Results indicated that night crashes,crashes outside urban areas as well as crashes involving bicycles or powered-two-wheelers are associated with higher injury severity of children and adolescents.Interestingly,crashes involving pedestrians are associated with lower injury severity than head-on collisions and run-off-road collisions with fixed objects.Side and sideswipe crashes also result in lower injury severities.The likelihood ratio test indicated that crash-related factors are instable when comparing the models utilizing data before and after 2010 respectively.This study contributes to the current knowledge in the field,as to the best of our knowledge this is the first study that addresses unobserved heterogeneity when analyzing child and adolescent injury severity.Overall,the findings of this study provide useful insights and could assist in unveiling crash risk factors and prioritize programs and measures to promote road safety of children and adolescents.
基金funded by the Wyoming Department of Transportation(WyDOT)supported by the Mountain Plains Consortium(Grant Number 69A3551747108(FAST Act))。
文摘Every year,a substantial number of children sustain injuries and fatalities in motor vehicle crashes in Wyoming.Understanding the factors contributing to child injury is crucial for the development of appropriate mitigation measures that aid in alleviating the severity of such injuries.In this study,a hierarchical Bayesian binary logit regression model was developed to investigate the factors that contribute to children’s injuries resulting from crashes while accounting for possible intra-class correlation effects(those of unobserved factors common to children involved in the same crash).A strong correlation among crashes justified the use of the hierarchical Bayesian logit model.As per the modeling results,the children’s ages,safety restraint types,vehicle types,drivers’ages,alcohol/drug involvement,drivers’seat belt use habits,drivers’actions,manners of collision and environmental conditions contributed to child injury risk.The child’s age was found to be inversely related to the risk of injury.Similarly,among safety restraint types,rear-facing car seats and forward-facing car seats were found to reduce injury likelihoods in crashes.When it comes to the drivers’characteristics,the probability of incurring injuries among the child population increased in the presence of young,unbuckled and impaired drivers.Furthermore,improper driving actions,such as running off the road,raised the risk of incurring injuries to children.The findings of this study may be beneficial to authorities regarding developing and implementing road safety programs aimed at ameliorating child injury concerns.
基金sponsored by the National Natural Science Foundation of China (Grant No.71901223)the Natural Science Foundation of Hunan Province (Grant No.2021JJ40746)the Postgraduate Research and Innovation Project of Central South University (Grant No.1053320216523).
文摘Improper lane-changing behaviours may result in breakdown of traffic flow and the occurrence of various types of collisions.This study investigates lane-changing behaviours of multiple vehicles and the stimulative effect on following drivers in a consecutive lanechanging scenario.The microscopic trajectory data from the HighD dataset are used for driving behaviour analysis.Two discretionary lane-changing vehicle groups constitute a consecutive lane-changing scenario,and not only distance-and speed-related factors but also driving behaviours are taken into account to examine the impacts on the utility of following lane-changing vehicles.A random parameters logit model is developed to capture the driver’s psychological heterogeneity in the consecutive lane-changing situation.Furthermore,a lane-changing utility prediction model is established based on three supervised learning algorithms to detect the improper lane-changing decision.Results indicate that 1)the consecutive lane-changing behaviours have a significant negative effect on the following lane-changing vehicles after lane change;2)the stimulative effect exists in a consecutive lane-change situation and its influence is heterogeneous due to different psychological activities of drivers;and 3)the utility prediction model can be used to detect an improper lane-changing decision.
文摘Bicycling has been actively promoted as a clean and efficient mode of commute.Besides,due to the personal and societal benefits it provides,it has been adopted by many city dwellers for short-distance trips.Despite the integral role this active transport mode plays,it is unfortunately associated with a high risk of fatalities in the event of a traffic crash as they are not protected.Many studies have been conducted in several jurisdictions to examine the factors contributing to crashes involving these vulnerable road users.In the case of Louisiana which is currently experiencing increased cases of severe and fatal bicycleinvolved crashes,less attention has been paid to investigating the critical factors influencing bicyclist injury severity outcomes using more detailed data and advanced econometric modeling frameworks to help propose adequate policies to improve the safety of riders.Against this background,this study examined the key contributing factors influencing bicyclist injuries by using more detailed roadway crash data spanning 2010-2016 obtained from the state of Louisiana.The study then applies an advanced random parameter logit modeling with heterogeneity in means and variances to address the unobserved heterogeneity issue associated with traffic crash data.To overcome the imbalanced data issue,three major crash injury levels were used instead of the conventional five crash injury levels.Besides,the data groups classified under each injury level were compared for the final variable selection.The study found that distracted drivers,elderly bicyclists,careless operations,and riding in dark conditions increase the probability of having severe injuries in vehicle-bicyclist crashes.Moreover,the variables for straight-level roadways and city streets decrease the odds of severe injuries.The straight-level roadway may provide better sight distance for both drivers and bicyclists,and complex environments like city streets discourage crashes with severe injuries.
