Root cause analysis with enriched process logs

In the field of process mining, the use of event logs for the purpose of root cause analysis is increasingly studied. In such an analysis, the availability of attributes/features that may explain the root cause of some phenomena is crucial. Currently, the process of obtaining these attributes from raw event logs is performed more or less on a case-by-case basis: there is still a lack of generalized systematic approach that captures this process. This paper proposes a systematic approach to enrich and transform event logs in order to obtain the required attributes for root cause analysis using classical data mining techniques, the classification techniques. This approach is formalized and its applicability has been validated using both self-generated and publicly-available logs.

Empirical analysis on relationship between traffic conditions and crash occurrences

This paper investigates relationship between traffic conditions and the crash occurrence likelihood (COL) using the I-880 data. To remedy the data limitations and the methodological shortcomings suffered by previous studies, a multiresolution data processing method is proposed and implemented, upon which binary logistic models were developed. The major findings of this paper are: 1) traffic conditions have significant impacts on COL at the study site; Specifically, COL in a congested (transitioning) traffic flow is about 6 (1.6) times of that in a free flow condition; 2)Speed variance alone is not sufficient to capture traffic dynamics’ impact on COL; a traffic chaos indicator that integrates speed, speed variance, and flow is proposed and shows a promising performance; 3) Models based on aggregated data shall be interpreted with caution. Generally, conclusions obtained from such models shall not be generalized to individual vehicles (drivers) without further evidences using high-resolution data and it is dubious to either claim or disclaim speed kills based on aggregated data.

Bayesian hierarchical analysis on crash prediction models

Traditional crash prediction models, such as generalized linear regression models, are incapable of taking into account the multilevel data structure, which extensively exists in crash data. Disregarding the possible within-group correlations can lead to the production of models giving unreliable and biased estimates of unknowns. This study innovatively proposes a -level hierarchy, viz. (Geographic region level – Traffic site level – Traffic crash level – Driver-vehicle unit level – Vehicle-occupant level) Time level, to establish a general form of multilevel data structure in traffic safety analysis. To properly model the potential cross-group heterogeneity due to the multilevel data structure, a framework of Bayesian hierarchical models that explicitly specify multilevel structure and correctly yield parameter estimates is introduced and recommended. The proposed method is illustrated in an individual-severity analysis of intersection crashes using the Singapore crash records. This study proved the importance of accounting for the within-group correlations and demonstrated the flexibilities and effectiveness of the Bayesian hierarchical method in modeling multilevel structure of traffic crash data.

Implications of lipid biology for the pathogenesis of schizophrenia

Objective: Preclinical and clinical data suggest that lipid biology is integral to brain development and neurodegeneration. Both aspects are proposed as being important in the pathogenesis of schizophrenia. The purpose of this paper is to examine the implications of lipid biology, in particular the role of essential fatty acids (EFA), for schizophrenia. Methods: Medline databases were searched from 1966 to 2001 followed by the crosschecking of references. Results: Most studies investigating lipids in schizophrenia described reduced EFA, altered glycerophospholipids and an increased activity of a calcium-independent phospholipase A2 in blood cells and in post-mortem brain tissue. Additionally, in vivo brain phosphorus-31 Magnetic Resonance Spectroscopy (31P-MRS) demonstrated lower phosphomonoesters (implying reduced membrane precursors) in first- and multi-episode patients. In contrast, phosphodiesters were elevated mainly in first-episode patients (implying increased membrane breakdown products), whereas inconclusive results were found in chronic patients. EFA supplementation trials in chronic patient populations with residual symptoms have demonstrated conflicting results. More consistent results were observed in the early and symptomatic stages of illness, especially if EFA with a high proportion of eicosapentaenoic acid was used. Conclusion: Peripheral blood cell, brain necropsy and 31P-MRS analysis reveal a disturbed lipid biology, suggesting generalized membrane alterations in schizophrenia. 31P-MRS data suggest increased membrane turnover at illness onset and persisting membrane abnormalities in established schizophrenia. Cellular processes regulating membrane lipid metabolism are potential new targets for antipsychotic drugs and might explain the mechanism of action of treatments such as eicosapentaenoic acid.

Analytical solutions for the multi-term time-space Caputo-Riesz fractional advection-diffusion equations on a finite domain

Generalized fractional partial differential equations have now found wide application for describing important physical phenomena, such as subdiffusive and superdiffusive processes. However, studies of generalized multi-term time and space fractional partial differential equations are still under development. In this paper, the multi-term time-space Caputo-Riesz fractional advection diffusion equations (MT-TSCR-FADE) with Dirichlet nonhomogeneous boundary conditions are considered. The multi-term time-fractional derivatives are defined in the Caputo sense, whose orders belong to the intervals [0, 1], [1, 2] and [0, 2], respectively. These are called respectively the multi-term time-fractional diffusion terms, the multi-term time-fractional wave terms and the multi-term time-fractional mixed diffusion-wave terms. The space fractional derivatives are defined as Riesz fractional derivatives. Analytical solutions of three types of the MT-TSCR-FADE are derived with Dirichlet boundary conditions. By using Luchko's Theorem (Acta Math. Vietnam., 1999), we proposed some new techniques, such as a spectral representation of the fractional Laplacian operator and the equivalent relationship between fractional Laplacian operator and Riesz fractional derivative, that enabled the derivation of the analytical solutions for the multi-term time-space Caputo-Riesz fractional advection-diffusion equations. © 2012.