Iowa Motorcycle Operator's Manual, 2014

The rules and regualtions for owning and operating a motorcycle in Iow

Iowa Drivers Manual, Revised June 2017

The rules and regulations for operating a motored vehicles in Iowa

Iowa Drivers Manual, 2013-2014

The rules and regulations for operating a motored vehicles in Iowa

Iowa Driver's Manual 2013-2014

This manual will provide you the information needed for a general understanding of the principles of safe and lawful operation of a motor vehicle. However, it is NOT intended to serve as a precise statement of Iowa statutes concerning the operation of a motor vehicle and should not be considered as such. For further information, check the Code of Iowa, Chapters 321 through 321J, and the Iowa Department of Transportation rules contained in the Iowa Administrative Code

Iowa Driver's Manual 2015-2016

This manual will provide you the information needed for a general understanding of the principles of safe and lawful operation of a motor vehicle. However, it is NOT intended to serve as a precise statement of Iowa statutes concerning the operation of a motor vehicle and should not be considered as such. For further information, check the Code of Iowa, Chapters 321 through 321J, and the Iowa Department of Transportation rules contained in the Iowa Administrative Code

Patterns of traumatic brain injury and six-month neuropsychological outcomes in Uganda

Abstract Background Traumatic brain injuries in Uganda are on the increase, however little is known about the neuropsychological outcomes in survivors. This study characterized patients with traumatic brain injury (TBI) and the associated six-month neuropsychological outcomes in a Ugandan tertiary hospital. Methods Patients admitted at Mulago Hospital with head injury from November 2015 to April 2016 were prospectively enrolled during admission and followed up at six months after discharge to assess cognition, posttraumatic stress symptoms (PTSS), depression symptoms and physical disability. The outcomes were compared to a non-head-injury group recruited from among the caretakers, siblings and neighbours of the patients with age and sex entered as covariates. Results One hundred and seventy-one patients and 145 non-head injury participants were enrolled. The age range for the whole sample was 1 to 69 years with the non-head injury group being older (mean age (SD) 33.34 (13.35) vs 29.34 (14.13) years of age, p = 0.01). Overall, motorcycle crashes (36/171, 38.6%) and being hit by an object (58/171, 33.9%) were the leading causes of TBI. Head injury from falls occurred more frequently in children < 18 years (13.8% vs 2.8%, p = 0.03). In adults 18 years and older, patients had higher rates of neurocognitive impairment (28.4% vs 6.6%, p < 0.0001), PTSS (43.9% vs 7.9%, p < 0.0001), depression symptoms (55.4% vs 10%, p < 0.0001) and physical disability (7.2% vs 0%, p = 0.002). Lower Glasgow Coma Score (GCS) on admission was associated with neurocognitive impairment (11.6 vs 13.1, p = 0.04) and physical disability (10 vs 12.9, p = 0.01) six months later. Conclusion This first such study in the East-African region shows that depth of coma on admission in TBI is associated with neurocognitive impairment and physical disability.https://deepblue.lib.umich.edu/bitstream/2027.42/147735/1/12883_2019_Article_1246.pd

DATA-DRIVEN BAYESIAN METHOD-BASED TRAFFIC CRASH DRIVER INJURY SEVERITY FORMULATION, ANALYSIS, AND INFERENCE

Traffic crashes have resulted in significant cost to society in terms of life and economic losses, and comprehensive examination of crash injury outcome patterns is of practical importance. By inferring the parameters of interest from prior information and studied datasets, Bayesian models are efficient methods in data analysis with more accurate results, but their applications in traffic safety studies are still limited. By examining the driver injury severity patterns, this research is proposed to systematically examine the applicability of Bayesian methods in traffic crash driver injury severity prediction in traffic crashes. In this study, three types of Bayesian models are defined: hierarchical Bayesian regression model, Bayesian non-regression model and knowledge-based Bayesian non-parametric model, and a conceptual framework is developed for selecting the appropriate Bayesian model based on discrete research purposes. Five Bayesian models are applied accordingly to test their effectiveness in traffic crash driver injury severity prediction and variable impact estimation: hierarchical Bayesian binary logit model, hierarchical Bayesian ordered logit model, hierarchical Bayesian random intercept model with cross-level interactions, multinomial logit (MNL)-Bayesian Network (BN) model, and decision table/na\xefve Bayes (DTNB) model. A complete dataset containing all crashes occurring on New Mexico roadways in 2010 and 2011 is used for model analyses. The studied dataset is composed of three major sub-datasets: crash dataset, vehicle dataset and driver dataset, and all included variables are therefore divided into two hierarchical levels accordingly: crash-level variables and vehicle/driver variables. From all these five models, the model performance and analysis results have shown promising performance on injury severity prediction and variable influence analysis, and these results underscore the heterogeneous impacts of these significant variables on driver injury severity outcomes. The performances of these models are also compared among these methods or with traditional traffic safety models. With the analyzed results, tentative suggestions regarding countermeasures and further research efforts to reduce crash injury severity are proposed. The research results enhance the understandings of the applicability of Bayesian methods in traffic safety analysis and the mechanisms of crash injury severity outcomes, and provide beneficial inference to improve safety performance of the transportation system