Improving Optimal Fingerprinting Methods Requires a Viewpoint beyond Statistical Science

While being successful in the detection and attribution of climate change,the optimal fingerprinting method(OFM)may have some limitations from a physics-and-dynamics-based viewpoint.Here,an analysis is made on the linearity,noninteraction,and stationary-variability assumptions adopted by OFM.It is suggested that furthering OFM needs a viewpoint beyond statistical science,and the method should be combined with theoretical tools in the dynamics and physics of the Earth system,so as to be applied for the detection and attribution of nonlinear climate change including tipping elements within the Earth system.

Construction of crash prediction model of freeway basic segment based on interactive influence of explanatory variables

In order to improve the prediction precision of the safety performance function （SPF） of freeway basic segments, design and crash data of 640 segments are collected from different institutions. Three negative binomial （NB） regression models and three generalized negative binomial （GNB） regression models are built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The effective use of the GNB model in analyzing the interactive influence of explanatory variables and predicting freeway basic segments is demonstrated. Among six models, the two models （one is the NB model and the other is the GNB model. ） which consider the interactive influence of the annual average daily traffic （AADT） and length are more reasonable for predicting results. Furthermore, a comprehensive study is carried out to prove that when considering the interactive influence, the NB and GNB models have almost the same fitting performance in estimating the crashes, among which the GNB model is slightly better for prediction performance.

Diabetes Risk Factor and Its Relationship to Increasing Coronavirus (COVID-19) Mortality Rate in United States in 2019-2022: An Epidemiological Study

As an effort to understand the effect of diabetes on the increasing rate of COVID-19 infection, we embarked upon a detailed statistical analysis of various datasets that include COVID-19 infection and mortality rate, diabetes and diseases that may contribute to the severity and risk factor of diabetes in individuals and this impact on COVID-19 and the mortality rate. These diseases include respiratory diseases, cardiovascular diseases, and obesity. Equally significant is the statistical analysis on ethnicity, age, and sex on COVID-19 infection as well as mortality rate. Their possible contributions to increasing the severity and risk factor of diabetes as a risk to mortality to individuals who have COVID-19. Objectives: The ultimate objectives of this investigation are as follow: 1) Is there a risk factor of diabetes on COVID-19 infection and increasing mortality rate? 2) To what extent do other disease conditions that include, obesity, heart failure, and respiratory diseases influence the severity and risk factor of diabetes on increasing COVID-19 infection and mortality rate? 3) To what extent does age, race, and gender increase the mortality of COVID-19 and increase the severity and risk factor of diabetes on COVID-19 mortality rate? 4) How and why COVID-19 virus increases the risk of diabetes in children? 5) Diabetes and COVID-19: Who is most at Risk? Lastly, understanding the misconception of COVID-19 and diabetes.

Application of Road Information in Ground Moving Target Tracking

A new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dynamic models modification （DMM VS-IMM for short）. Firstly, road information is employed to modify the target dynamic models used by filter, including modification of state transition matrix and process noise. Secondly, road information is applied to update the model set of a VS-IMM estimator. Predicted state estimation and road information are used to locate the target in the road network on which the model set is updated and finally IMM filtering is implemented. As compared with traditional methods, the accuracy of state estimation is improved for target moving not only on a single road, but also through an intersection. Monte Carlo simulation demonstrates the efficiency and robustness of the proposed algorithm with moderate computational loads.

Variable and Robust East Asian Monsoon Rainfall Response to El Nio over the Past 60 Years(1957–2016)

Severe flooding occurred in southern and northern China during the summer of 2016 when the 2015 super El Nio decayed to a normal condition. However, the mean precipitation during summer（June–July-August） 2016 does not show significant anomalies, suggesting that — over East Asia（EA） — seasonal mean anomalies have limited value in representing hydrological hazards. Scrutinizing season-evolving precipitation anomalies associated with 16 El Nio episodes during 1957–2016 reveals that, over EA, the spatiotemporal patterns among the four categories of El Nio events are quite variable, due to a large range of variability in the intensity and evolution of El Nio events and remarkable subseasonal migration of the rainfall anomalies. The only robust seasonal signal is the dry anomalies over central North China during the El Nio developing summer. Distinguishing strong and weak El Nio impacts is important. Only strong El Nio events can persistently enhance EA subtropical frontal precipitation from the peak season of El Nio to the ensuing summer, by stimulating intense interaction between the anomalous western Pacific anticyclone（WPAC） and underlying dipolar sea surface temperature anomalies in the Indo-Pacific warm pool, thereby maintaining the WPAC and leading to a prolonged El Nio impact on EA. A weak El Nio may also enhance the post-El Nio summer rainfall over EA, but through a different physical process： the WPAC re-emerges as a forced response to the rapid cooling in the eastern Pacific. The results suggest that the skillful prediction of rainfall over continental EA requires the accurate prediction of not only the strength and evolution of El Nio, but also the subseasonal migration of EA rainfall anomalies.

MINIMUM ATTRIBUTE CO-REDUCTION ALGORITHM BASED ON MULTILEVEL EVOLUTIONARY TREE WITH SELF-ADAPTIVE SUBPOPULATIONS

Attribute reduction is an important process in rough set theory.Finding minimum attribute reduction has been proven to help the user-oriented make better knowledge discovery in some cases.In this paper,an efficient minimum attribute reduction algorithm is proposed based on the multilevel evolutionary tree with self-adaptive subpopulations.A model of multilevel evolutionary tree with self-adaptive subpopulations is constructed,and interacting attribute sets are better decomposed into subsets by the self-adaptive mechanism of elitist populations.Moreover it can self-adapt the subpopulation sizes according to the historical performance record so that interacting attribute decision variables are captured into the same grouped subpopulation,which will be extended to better performance in both quality of solution and competitive computation complexity for minimum attribute reduction.The conducted experiments show the proposed algorithm is better on both efficiency and accuracy of minimum attribute reduction than some representative algorithms.Finally the proposed algorithm is applied to magnetic resonance image(MRI)segmentation,and its stronger applicability is further demonstrated by the effective and robust segmentation results.

AN INTERMOLECULAR ^(13)C{~1H} NUCLEAR OVERHAUSER EFFECT FOR THE CARBON OF CCl_4 IN POLYMER SOLUTIONS

An intermolecular ^(13)C{~1H} NOE of CCl_4 in the solutions of polystyrene and polybutadiene and their copolymers was observed. The results show that the defined polymer-CCl_4 interaction variable has a linear relation with the polymer composition and the difference of solubility parameters and exponentially depends on the reciprocal of temperature.

Non-completely elastic interactions in a(2+1)-dimensional dispersive long wave equation

With the help of a modified mapping method, we obtain two kinds of variable separation solutions with two arbitrary functions for the （24-1）-dimensional dispersive long wave equation. When selecting appropriate multi-valued functions in the variable separation solution, we investigate the interactions among special multi-dromions, dromion-like multi-peakons, and dromion-like multi-semifoldons, which all demonstrate non-completely elastic properties.

Generation of Multipartite Entangled States Using Switchable Coupling of Cooper-Pair-Boxes

We propose a scheme for a switchable coupling between several Cooper-pair boxes in the charge regime. The switch is embodied in a SQUID element contained in the center conductor of a transmission-line resonator. Altering the flux bias through the SQUID allows for changing the effective resonator length. Thus the position of the nodes and anti-nodes of the relevant eigenmodes changes and leads to a variable interaction strength between qubit and cavity vacuum field. For the coupled qubits the interaction is dispersive. An example for the application of this switch is the generation of multipartite entangled states for three and four charge qubits. Although used as a discrete switch in the present proposal, the combined system of SQUID module integrated into the transmission line may be operated continuously as well.

Ground Moving Target Tracking with VS-IMM Using Mean Shift Unscented Particle Filter

In order to track ground moving target, a variable structure interacting multiple model （VS-IMM） using mean shift unscented particle filter （MS-UPF） is proposed in this paper. In model-conditioned filtering, sample particles obtained from the unscented particle filter are moved towards the maximal posterior density estimation of the target state through mean shift. On the basis of stop model in VS-IMM, hide model is proposed. Once the target is obscured by terrain, the prediction at prior time is used instead of the measurement at posterior time; in addition, the road model set used is not changed. A ground moving target indication （GMTI） radar is employed in three common simulation scenarios of ground target： entering or leaving a road, crossing a junction and no measurement. Two evaluation indexes, root mean square error （RMSE） and average normalized estimation error squared （ANEES）, are used. The results indicate that when the road on which the target moving changes, the tracking accuracy is effectively improved in the proposed algorithm. Moreover, track interruption could be avoided if the target is moving too slowly or masked by terrain.

Interaction Behaviors Between Special Dromions in the(2+1)-Dimensional Broer-Kaup-Kupershmidt Equation

A modified mapping method is used to obtain variable separation solution with two arbitrary functions of the(2+1)-dimensional Broer-Kaup-Kupershmidt equation.Based on the variable separation solution and by selecting appropriate functions,we discuss the completely elastic head-on collision between two dromion-lattices,non-completely elastic "chase and collision" between two multi-dromion-pairs and completely non-elastic interaction phenomenon between anti-dromion and dromion-pair.