Graphical Model Inference with Erosely Measured Data

In this paper, we investigate the Gaussian graphical model inference problem in a novel setting that we call erose measurements, referring to irregularly measured or observed data. For graphs, this results in different node pairs having vastly different sample sizes which frequently arises in data integration, genomics, neuroscience, and sensor networks. Existing works characterize the graph selection performance using the minimum pairwise sample size, which provides little insights for erosely measured data, and no existing inference method is applicable. We aim to fill in this gap by proposing the first inference method that characterizes the different uncertainty levels over the graph caused by the erose measurements, named GI-JOE (Graph Inference when Joint Observations are Erose). Specifically, we develop an edge-wise inference method and an affiliated FDR control procedure, where the variance of each edge depends on the sample sizes associated with corresponding neighbors. We prove statistical validity under erose measurements, thanks to careful localized edge-wise analysis and disentangling the dependencies across the graph. Finally, through simulation studies and a real neuroscience data example, we demonstrate the advantages of our inference methods for graph selection from erosely measured data.</p

Smart Zwitterionic Membranes with On/Off Behavior for Protein Transport

Poly(acrylonitrile) (PAN)-based zwitterionic membranes, composed of PAN and poly(N,N-dimethyl-N-methacryloxyethyl-N-(3-sulfopropyl) copolymer, are electrolyte-sensitive smart membranes. The hydrophilicity was increased and protein adsorption was remarkably decreased for the membranes in response to environmental stimuli. FTIR spectroscopic analysis directly provided molecular-level observation of the enhanced dissociation and hydration of zwitterionic sulfobetaine dipoles at higher electrolyte concentrations. The smart PAN-based zwitterionic membranes can close or open channels for protein transport under different NaCl concentrations. The electrolyte-sensitive switch of on/off behavior for protein transport is reversible

SEM results for cluster 1.

Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect mechanisms of the factors in the risk scenarios have not been completely explained. Therefore, this study used the K-means algorithm to perform multidimensional feature homogeneous clustering for drivers involved in crashes and near-crashes. Structural equation modeling involving mediating effects was introduced to explore the direct and indirect effects of each influencing factor on vehicle crashes under risk scenarios and compare the differences in crash causation among different driver clusters. The results indicate that the drivers who experienced the risk scenarios can be classified into two homogeneous driver clusters. Significant differences exist in the demographic characteristics, intrinsic driving characteristics, and crash rates between them. In the risk scenario, traffic factors, distraction state, crash avoidance reaction, and maneuver judgment directly affect the crash outcomes of the two cluster drivers. Demographic characteristics and environmental factors have fewer direct influence on the crash outcomes of two-cluster drivers, but produce more complex mediating effects. Analysis of the differences in the influence of factors between clusters indicates that the fundamental cause of crashes for cluster 1 drivers includes poor driving skills. In contrast, cluster 2 drivers’ crashes were more influenced by traffic conditions and their safety awareness. The analysis method of this study can be used to develop more targeted road safety policies to reduce the occurrence of vehicle crashes.</div

Driver descriptive statistics.

Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect mechanisms of the factors in the risk scenarios have not been completely explained. Therefore, this study used the K-means algorithm to perform multidimensional feature homogeneous clustering for drivers involved in crashes and near-crashes. Structural equation modeling involving mediating effects was introduced to explore the direct and indirect effects of each influencing factor on vehicle crashes under risk scenarios and compare the differences in crash causation among different driver clusters. The results indicate that the drivers who experienced the risk scenarios can be classified into two homogeneous driver clusters. Significant differences exist in the demographic characteristics, intrinsic driving characteristics, and crash rates between them. In the risk scenario, traffic factors, distraction state, crash avoidance reaction, and maneuver judgment directly affect the crash outcomes of the two cluster drivers. Demographic characteristics and environmental factors have fewer direct influence on the crash outcomes of two-cluster drivers, but produce more complex mediating effects. Analysis of the differences in the influence of factors between clusters indicates that the fundamental cause of crashes for cluster 1 drivers includes poor driving skills. In contrast, cluster 2 drivers’ crashes were more influenced by traffic conditions and their safety awareness. The analysis method of this study can be used to develop more targeted road safety policies to reduce the occurrence of vehicle crashes.</div

CNC data descriptive statistics.

Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect mechanisms of the factors in the risk scenarios have not been completely explained. Therefore, this study used the K-means algorithm to perform multidimensional feature homogeneous clustering for drivers involved in crashes and near-crashes. Structural equation modeling involving mediating effects was introduced to explore the direct and indirect effects of each influencing factor on vehicle crashes under risk scenarios and compare the differences in crash causation among different driver clusters. The results indicate that the drivers who experienced the risk scenarios can be classified into two homogeneous driver clusters. Significant differences exist in the demographic characteristics, intrinsic driving characteristics, and crash rates between them. In the risk scenario, traffic factors, distraction state, crash avoidance reaction, and maneuver judgment directly affect the crash outcomes of the two cluster drivers. Demographic characteristics and environmental factors have fewer direct influence on the crash outcomes of two-cluster drivers, but produce more complex mediating effects. Analysis of the differences in the influence of factors between clusters indicates that the fundamental cause of crashes for cluster 1 drivers includes poor driving skills. In contrast, cluster 2 drivers’ crashes were more influenced by traffic conditions and their safety awareness. The analysis method of this study can be used to develop more targeted road safety policies to reduce the occurrence of vehicle crashes.</div

Driving characteristic descriptive statistics.

Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect mechanisms of the factors in the risk scenarios have not been completely explained. Therefore, this study used the K-means algorithm to perform multidimensional feature homogeneous clustering for drivers involved in crashes and near-crashes. Structural equation modeling involving mediating effects was introduced to explore the direct and indirect effects of each influencing factor on vehicle crashes under risk scenarios and compare the differences in crash causation among different driver clusters. The results indicate that the drivers who experienced the risk scenarios can be classified into two homogeneous driver clusters. Significant differences exist in the demographic characteristics, intrinsic driving characteristics, and crash rates between them. In the risk scenario, traffic factors, distraction state, crash avoidance reaction, and maneuver judgment directly affect the crash outcomes of the two cluster drivers. Demographic characteristics and environmental factors have fewer direct influence on the crash outcomes of two-cluster drivers, but produce more complex mediating effects. Analysis of the differences in the influence of factors between clusters indicates that the fundamental cause of crashes for cluster 1 drivers includes poor driving skills. In contrast, cluster 2 drivers’ crashes were more influenced by traffic conditions and their safety awareness. The analysis method of this study can be used to develop more targeted road safety policies to reduce the occurrence of vehicle crashes.</div

Results of varimax rotation factor analysis.

Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect mechanisms of the factors in the risk scenarios have not been completely explained. Therefore, this study used the K-means algorithm to perform multidimensional feature homogeneous clustering for drivers involved in crashes and near-crashes. Structural equation modeling involving mediating effects was introduced to explore the direct and indirect effects of each influencing factor on vehicle crashes under risk scenarios and compare the differences in crash causation among different driver clusters. The results indicate that the drivers who experienced the risk scenarios can be classified into two homogeneous driver clusters. Significant differences exist in the demographic characteristics, intrinsic driving characteristics, and crash rates between them. In the risk scenario, traffic factors, distraction state, crash avoidance reaction, and maneuver judgment directly affect the crash outcomes of the two cluster drivers. Demographic characteristics and environmental factors have fewer direct influence on the crash outcomes of two-cluster drivers, but produce more complex mediating effects. Analysis of the differences in the influence of factors between clusters indicates that the fundamental cause of crashes for cluster 1 drivers includes poor driving skills. In contrast, cluster 2 drivers’ crashes were more influenced by traffic conditions and their safety awareness. The analysis method of this study can be used to develop more targeted road safety policies to reduce the occurrence of vehicle crashes.</div

Results for the mediation effect.

Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect mechanisms of the factors in the risk scenarios have not been completely explained. Therefore, this study used the K-means algorithm to perform multidimensional feature homogeneous clustering for drivers involved in crashes and near-crashes. Structural equation modeling involving mediating effects was introduced to explore the direct and indirect effects of each influencing factor on vehicle crashes under risk scenarios and compare the differences in crash causation among different driver clusters. The results indicate that the drivers who experienced the risk scenarios can be classified into two homogeneous driver clusters. Significant differences exist in the demographic characteristics, intrinsic driving characteristics, and crash rates between them. In the risk scenario, traffic factors, distraction state, crash avoidance reaction, and maneuver judgment directly affect the crash outcomes of the two cluster drivers. Demographic characteristics and environmental factors have fewer direct influence on the crash outcomes of two-cluster drivers, but produce more complex mediating effects. Analysis of the differences in the influence of factors between clusters indicates that the fundamental cause of crashes for cluster 1 drivers includes poor driving skills. In contrast, cluster 2 drivers’ crashes were more influenced by traffic conditions and their safety awareness. The analysis method of this study can be used to develop more targeted road safety policies to reduce the occurrence of vehicle crashes.</div

Fit statistics for SEM.

Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect mechanisms of the factors in the risk scenarios have not been completely explained. Therefore, this study used the K-means algorithm to perform multidimensional feature homogeneous clustering for drivers involved in crashes and near-crashes. Structural equation modeling involving mediating effects was introduced to explore the direct and indirect effects of each influencing factor on vehicle crashes under risk scenarios and compare the differences in crash causation among different driver clusters. The results indicate that the drivers who experienced the risk scenarios can be classified into two homogeneous driver clusters. Significant differences exist in the demographic characteristics, intrinsic driving characteristics, and crash rates between them. In the risk scenario, traffic factors, distraction state, crash avoidance reaction, and maneuver judgment directly affect the crash outcomes of the two cluster drivers. Demographic characteristics and environmental factors have fewer direct influence on the crash outcomes of two-cluster drivers, but produce more complex mediating effects. Analysis of the differences in the influence of factors between clusters indicates that the fundamental cause of crashes for cluster 1 drivers includes poor driving skills. In contrast, cluster 2 drivers’ crashes were more influenced by traffic conditions and their safety awareness. The analysis method of this study can be used to develop more targeted road safety policies to reduce the occurrence of vehicle crashes.</div

SEM results for cluster 2.

Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect mechanisms of the factors in the risk scenarios have not been completely explained. Therefore, this study used the K-means algorithm to perform multidimensional feature homogeneous clustering for drivers involved in crashes and near-crashes. Structural equation modeling involving mediating effects was introduced to explore the direct and indirect effects of each influencing factor on vehicle crashes under risk scenarios and compare the differences in crash causation among different driver clusters. The results indicate that the drivers who experienced the risk scenarios can be classified into two homogeneous driver clusters. Significant differences exist in the demographic characteristics, intrinsic driving characteristics, and crash rates between them. In the risk scenario, traffic factors, distraction state, crash avoidance reaction, and maneuver judgment directly affect the crash outcomes of the two cluster drivers. Demographic characteristics and environmental factors have fewer direct influence on the crash outcomes of two-cluster drivers, but produce more complex mediating effects. Analysis of the differences in the influence of factors between clusters indicates that the fundamental cause of crashes for cluster 1 drivers includes poor driving skills. In contrast, cluster 2 drivers’ crashes were more influenced by traffic conditions and their safety awareness. The analysis method of this study can be used to develop more targeted road safety policies to reduce the occurrence of vehicle crashes.</div