IMR enables significant reduction of the image noise and improvement of image quality in sub-mSv (66% reduction) chest scans.

The aims of this study were to achieve a quantitative assessment of the severity of accidents involving roadside trees on highways and to propose corresponding safety measures to reduce accident losses. This paper used the acceleration severity index (ASI), head injury criteria (HIC) and chest resultant acceleration (CRA) as indicators of occupant injuries and horizontal radii, vehicle departure speeds, tree diameters and roadside tree spacing as research variables to carry out bias collision tests between cars, trucks and trees by constructing a vehicle rigid body system and an occupant multibody system in PC-crash 10.0® simulation software. A total of 2,256 data points were collected. For straight and curved segments of highways, the occupant injury evaluation models of cars were fitted based on the CRA, and occupant injury evaluation models of trucks and cars were fitted based on the ASI. According to the Fisher optimal segmentation method, reasonable classification standards of severities of accidents involving roadside trees and the corresponding ASI and CRA thresholds were determined, and severity assessment methods for accidents involving roadside trees based on the CRA and ASI were provided. Additionally, a new index by which to evaluate the accuracy of the accident severity classification and the degree of misclassification was built and applied for the validity verification of the proposed severity assessment methods. A proportion of trucks was introduced to further improve the ASI evaluation model. For the same simulation conditions, the results show that driver chest injuries are more serious than driver head injuries and that the average ASI of cars is greater than that of trucks. The CRA and ASI have a positive linear correlation with the departure speed and a logarithmic correlation with the roadside tree diameters. The larger the spacing of roadside trees is and the smaller the horizontal radius is, the smaller the chance that a vehicle will experience a second collision and the lower the risk of occupant injury. In method validation, the evaluation results from two proposed severity assessment methods based on the CRA and ASI are consistent, and the degrees of misclassification are 4.65% and 4.26%, respectively, which verifies the accuracy of the methods proposed in this paper and confirms

ObjectiveTo evaluate a quantitative method for individually adjusting the tube current to obtain images with consistent noise in electrocardiogram (ECG)-gated CT cardiac scans.Materials and MethodsThe image noise from timing bolus and cardiac CT scans of 80 patients (Group A) who underwent a 64-row multidetector (MD) CT cardiac examination with patient-independent scan parameters were analyzed. A formula was established using the noise correlation between the timing bolus and cardiac scans. This formula was used to predict the required tube current to obtain the desired cardiac CT image noise based on the timing bolus noise measurement. Subsequently, 80 additional cardiac patients (Group B) were scanned with individually adjusted tube currents using an established formula to evaluate its ability to obtain accurate and consistent image noise across the patient population. Image quality was evaluated using score scale of 1 to 5 with a score of 3 or higher being clinically acceptable.ResultsUsing the formula, we obtained an average CT image noise of 28.55 Hounsfield unit (HU), with a standard deviation of only 1.7 HU, as opposed to a target value of 28 HU. Image quality scores were 4.03 and 4.27 for images in Groups A and B, respectively, and there was no statistical difference between the image quality scores between the two groups. However, the average CT dose index (CTDIvol) was 30% lower for Group B.ConclusionAdjusting the tube current based on timing bolus scans may provide a consistent image quality and dose optimization for cardiac patients of various body mass index values.

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In order to improve the driver's physiological and psychological state, the driver's mental load which is caused by sight distance, lighting, and other factors in the tunnel environment should be quantified via modeling the spatiotemporal data. e experimental schemes have been scientifically designed based on methods of traffic engineering and human factor engineering, which aims to test the driver's spatiotemporal data of eye movement and ECG (electrocardiogram) index in the tunnel environment. Firstly, the changes in the driver's spatiotemporal data are analyzed to judge the changing trend of the driver's workload in the tunnel environment. e results show that the cubic spline interpolation function model can fit the dynamic changes of average pupil diameter and heart rate (HR) growth rate well, and the goodness of fit for the model group is above 0.95. So, tunnel environment makes the driver's typical physiological indicators fluctuate in the coordinates of time and space, which can be modeled and quantified. Secondly, in order to analyze the classification of tunnel risk level, a fusion model has been built based on the functions of average pupil diameter and HR growth rate. e tunnel environmental risk level has been divided into four levels via the fusion model, which can provide a guidance for the classification of tunnel risk level. Furthermore, the fusion model allows tunnel design and construction personnel to adopt different safety design measures for different risk levels, and this method can effectively improve the economy of tunnel operating safety design.

Due to the limited and inconsistent evidence regarding DCTs' effects on intersection safety and efficiency, it is not sufficient to recommend any type of DCT to be installed at signalized intersections to improve safety and operational efficiency. Nevertheless, it is apparent that both RSCTs and CCTs enhance intersection capacity, though their impacts on intersection safety are unclear. Future studies need to further verify those anticipated safe and operational benefits of DCTs with enriched field observation data.

Traffic congestion, which has a direct impact on the driver’s mood and action, has become a serious problem in rush hours in most cities of China. Currently, the study about driver’s mood and action in traffic congestion is scarce, so it is necessary to work on the relationship among driver’s mood and action and traffic congestion. And the PSR (pressure-state-response) framework is established to describe that relationship. Here, PSR framework is composed of a three-level logical structure, which is composed of traffic congestion environment, drivers’ physiology change, and drivers’ behavior change. Based on the PSR framework, various styles of drivers have been chosen to drive on the congested roads, and then traffic stream state, drivers’ physiology, and behavior characters have been measured via the appropriative equipment. Further, driver’s visual characteristics and lane changing characteristics are analyzed to determine the parameters of PSR framework. According to the PSR framework, the changing law of drivers’ characteristics in traffic congestion has been obtained to offer necessary logical space and systematic framework for traffic congestion management.

The severe impact of traffic accidents, along with a large number of deaths and disabilities, necessitates further improvements in rescue path optimization. To make the emergency rescue more efficient and furthermore ensure health care in life-saving and mitigating traffic congestion as soon as possible, a methodology for rescue vehicle path optimization, timing co-evolutionary path optimization (TCEPO), is proposed to optimize the rescue path. Distinguishing from conventional online re-optimization (OLRO) and co-evolutionary path optimization (CEPO), in TCEPO, each optimization process co-evolves with future traffic environment that keeps changing over time, and the best path will be modified timely based on the predicted routing environmental dynamics (PRED) and recent traffic data. Besides, for better computation efficiency, this research reports an improved ripple spreading algorithm (RSA) as a realization of TCEPO to resolve the optimality problem. The modeling and solutions of TCEPO are discussed in detail to illustrate the applications in emergency rescue path optimization. In order to compare the performance of three methods (OLRO, CEPO and TCEPO), the same optimization tasks and scenarios are presented, and numerical simulation is carried out 100 times. Experimental results clearly prove that the proposed TCEPO possesses stronger robustness and is about 17.65% to 40.02% shorter than CEPO, as well as about 26.34% to 38.47% shorter than OLRO in terms of the travelling time under the PRED with various uncertainties. These advantages have a great impact on raising efficiency and reliability of emergency rescue, which can help rescue vehicles reach the destination as quickly as possible and save more lives.