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A rough road surface produces strong or continuous vibration in an ambulance, which causes feeling of discomfort, severe pain or sometimes deterioration of physical condition to injured or ill persons. With the aim of reducing such harmful vibration for safe and reliable medical emergency transportation services, this paper proposes a method to find out rough road surfaces from drive data of ambulances and to give them the priority for future pavement repair. There are four steps in the prioritization process: The first step is to collect drive data including information on location, speed and vibration with a smartphone. According to ISO2631-1, the vibration is quantified as a running r.m.s of the frequency-weighted acceleration. In the second step, meaningless data such as small vibration and extremely slow speed data are removed. Then, depending on the location and the traveling direction of the ambulance when the data were recorded, the data form clusters, which are candidates of road repair zones. Finally, each cluster is prioritized by the evaluation function. The larger amount of strong-vibration and slow-speed data are included in the cluster, the higher priority is given to it. An example of application to three ambulances is presented. The high priority clusters were well matched to the road surfaces that the ambulance crews were aware of as strong vibration zones. It is also shown that at least one and a half months are required in collection of the drive data to decide on the top ten clusters.

As a road pavement assessment tool, this paper presents an equation to estimate the vibrational ride comfort of local fixed-route buses from the international roughness index (IRI) and the bus speed. Before construction of the equation, all the needed data was collected as big data: The bus data such as acceleration, speed and position have been collected with the built-in accelerometer and GPS sensor of a smartphone for about 100 days on three national routes. After the measurement, according to ISO2631-1, the ride comfort was quantified as a running r.m.s of the frequency-weighted acceleration on a passenger seat. The IRI was measured every 10 meters with the Class 3 method. It was observed that the ride comfort was weakly correlated with the speed and moderately correlated with the IRI. For the large and the medium buses, two estimate equations were constructed in the form of a function with arguments of the bus speed and the IRI. Their parameters were determined by applying the data fitting technique to the collected big data. The estimate equations give the correlation coefficients of 0.70 to 0.79 and the root mean squared errors of 0.08 to 0.13 m/s2 between the mean values of the measured ride comfort and their estimates on the 10-meter road segments.

To improve patient safety in ambulance transport service, this paper proposes a new method for finding an optimal route of an ambulance depending on the disease or injury of a patient. Three transport cases are considered in this paper: Heart disease (HD), hemorrhagic cerebrovascular accident (HCVA) and fracture/dislocation (Fx/dx). The route optimality is measured with three evaluation functions. The first function evaluates an elapsed time from the scene of an emergency to a hospital. The second evaluates a blood pressure variation induced by the acceleration/deceleration motion of an ambulance. The third evaluates a compression load acting on the back of a patient due to the centrifugal force. They are calculated by applying the traveling model of an ambulance to the three-dimensional road network. The optimal route is selected in two stages. First, the candidate routes are found by solving the multiobjective optimization problem of minimizing the three evaluation functions. If the completely optimal solution (route), which minimizes the three functions simultaneously, is found, the selection process is finished. Otherwise, obtain the Pareto solutions to go to the second stage, in which a preference solution is selected from the Pareto solutions depending on the disease or injury of a patient. In the numerical example, the route selection is shown for HD, HCVA and Fx/dx. For the reference sake, the real routes of the ambulance recorded in Hiroshima City and the shortest routes obtained by Google Maps Navigation are also presented and compared to each other.

In preparation for optimal routing of patients to a hospital, this paper presents a traveling model of the ambulance that moves from the scene of an emergency to a hospital. The model is given in the form of a speed diagram along the route and constructed based on some rules: The ambulance moves with either a constant speed, a constant acceleration speed or a constant deceleration speed. The maximum speed changes depending on the legal speed limit for private vehicles. The ambulance decreases its speed to a specified level when it passes through an intersection with traffic lights or turns left or right. The model has eight adjustable parameters. They were optimized in the framework of the multi-objective optimization problem involving three objective functions, which are defined as modeling errors relevant to the traveling time, the fluctuation of blood pressure and the pressing force acting on the back of a patient. The genetic algorithm was used to obtain Pareto solutions to the problem. An optimal set of parameters was selected from among them. The validity of the model was confirmed by checking the objective functions for the validation data set. However, there was a limitation: The model does not match to the actual speed of the ambulance in such a case that it moves on the road where many traffic lights are placed close to each other and controlled synchronously.

This paper proposes the drive training assistant system for ambulance drivers to avoid the excessive blood pressure variation of patients caused by brake operation. The system has two functions. The first is to estimate the blood pressure variation in real time based the acceleration of a car and to inform a driver with an electric sound when the estimated blood pressure variation exceeds the pre-specified level. The second function is to inform a driver of the start timing to apply a brake with a synthetic sound when the distance to a next intersection is less than the brake distance necessary to keep the blood pressure variation below the pre-specified bound. The brake distance is calculated in real time based on the present speed of the car, the passing speed at the next intersection, the slope angle of a road and the pre-given tolerable blood pressure variation. This paper mainly focuses on the second function and its availability. The prototype system was constructed using a commercial smartphone with a GPS sensor, a triaxial accelerometer and a speaker. The availability of the system was examined through the car experiment. It was confirmed that, by starting a brake operation after the brake timing announcement of the system, the blood pressure variation was controlled below the pre-specified tolerable bound.

Oxidative stress is a major determinant of acute kidney injury (AKI); however, the effects of an AKI on renal redox system are unclear, and few existing AKI markers are suitable for evaluating oxidative stress. We measured urinary levels of the redox-regulatory protein thioredoxin 1 (TRX1) in patients with various kinds of kidney disease and in mice with renal ischemiareperfusion injury. Urinary TRX1 levels were markedly higher in patients with AKI than in those with chronic kidney disease or in healthy subjects. In a receiver operating characteristic curve analysis to differentiate between AKI and other renal diseases, the area under the curve for urinary TRX1 was 0.94 (95% confidence interval, 0.90-0.98), and the sensitivity and specificity were 0.88 and 0.88, respectively, at the optimal cutoff value of 43.0 μg/g creatinine. Immunostaining revealed TRX1 to be diffusely distributed in the tubules of normal kidneys, but to be shifted to the brush borders or urinary lumen in injured tubules in both mice and humans with AKI. Urinary TRX1 in AKI was predominantly in the oxidized form. In cultured human proximal tubular epithelial cells, hydrogen peroxide specifically and dose dependently increased TRX1 levels in the culture supernatant, while reducing intracellular levels. These findings suggest that urinary TRX1 is an oxidative stress-specific biomarker useful for distinguishing AKI from chronic kidney disease and healthy kidneys. [ABSTRACT FROM AUTHOR]