Effect of treadwear grade on the generation of tire PM emissions in laboratory and real-world driving conditions.

Tires generally wear out due to the friction between the tire and the road surface. Minimizing tire wear could reduce the non-exhaust particulate matter (PM) emissions from tires. Typically, tire treadwear grade can be used as an indicator of PM emissions from tires. Tires that wear out quickly will produce higher PM emissions than more durable tires. In this study, the effect of treadwear grade on the generation of tire PM emissions was investigated through laboratory and on-road driving measurements. In the laboratory measurements, a tire wear simulator installed in an enclosed chamber was used to eliminate artifacts caused by interfering particles during the generation and measurement of tire wear particles. For realistic on-road driving measurements, a mobile sampling vehicle was employed to sample road dust. The road dust was chemically analyzed using pyrolysis gas chromatography-mass spectrometry (GC-MS) to characterize the tire-road wear particles. Both measurements showed that the higher treadwear grade generated lower tire PM emissions due to the high strength of the rubber, except for the UTQG 700 tire. The UTQG 700 tire, which had the highest treadwear grade, produced higher PM emissions than the UTQG 350 and 500 tires because it readily formed the fine particles due to lamellar peeling rather than tearing or curling of tire treads. Notably, tire nanoparticles were observed in laboratory measurements due to the volatilization and nucleation of the sulphur (S) and zinc (Zn) compounds in the tire tread due to the frictional heat between the tire and paved road surface.

Comparison of total PM emissions emitted from electric and internal combustion engine vehicles: An experimental analysis.

Electric vehicles (EVs) are regarded as zero emission vehicles due to the absence of exhaust emissions. However, they still contribute non-exhaust particulate matter (PM) emissions, generated by brake wear, tire wear, road wear, and resuspended road dust. In fact, because EVs are heavier than internal combustion engine vehicles (ICEVs), their non-exhaust emissions are like to be even higher. While total PM emissions, including exhaust and non-exhaust PM emissions, from ICEVs and EVs have been compared based on the emission factors (EFs) listed in national emission inventories, there have been no comparisons based on experimental determinations. In this study, exhaust and non-exhaust emissions generated from a gasoline ICEV, diesel ICEV, and EV were experimentally investigated. The results showed that the EFs for the total PM emissions of ICEVs and EV were dependent on the inclusion of secondary exhaust PM, the brake pad type, and the regenerative braking intensity of the EV. When only primary exhaust PM emissions were considered in vehicles equipped with non-asbestos organic (NAO) brake pads, the total PM10 EF of the EV (47.7-49.3 mg/V·km) was 10-17 % higher than those of the gasoline ICEV (42.3 mg/V·km) and diesel ICEV (43.2 mg/V·km). However, in vehicles equipped with low-metallic (LM) brake pads, the total PM10 EF of the EV (49.2-57.7 mg/V·km) was comparable or lower than those of the gasoline ICEV (56.3 mg/V·km) and diesel ICEV (57.2 mg/V·km). When secondary PM emissions were included, the EF was always significantly lower for the EV than ICEVs. The total PM10 EF of the EV (47.7-57.7 mg/V·km) was lower than those of the gasoline ICEV (56.5-70.5 mg/V·km) and diesel ICEV (58.0-72.0 mg/V·km). Since secondary PM particles are mostly of submicron size, the EFs of the PM2.5 fraction of the ICEVs (28.7-33.0 mg/V·km) were two times higher than those of the EV (13.9-17.4 mg/V·km).

Development and operation of a digital platform for sharing pathology image data.

BACKGROUND: Artificial intelligence (AI) research is highly dependent on the nature of the data available. With the steady increase of AI applications in the medical field, the demand for quality medical data is increasing significantly. We here describe the development of a platform for providing and sharing digital pathology data to AI researchers, and highlight challenges to overcome in operating a sustainable platform in conjunction with pathologists. METHODS: Over 3000 pathological slides from five organs (liver, colon, prostate, pancreas and biliary tract, and kidney) in histologically confirmed tumor cases by pathology departments at three hospitals were selected for the dataset. After digitalizing the slides, tumor areas were annotated and overlaid onto the images by pathologists as the ground truth for AI training. To reduce the pathologists' workload, AI-assisted annotation was established in collaboration with university AI teams. RESULTS: A web-based data sharing platform was developed to share massive pathological image data in 2019. This platform includes 3100 images, and 5 pre-processing algorithms for AI researchers to easily load images into their learning models. DISCUSSION: Due to different regulations among countries for privacy protection, when releasing internationally shared learning platforms, it is considered to be most prudent to obtain consent from patients during data acquisition. CONCLUSIONS: Despite limitations encountered during platform development and model training, the present medical image sharing platform can steadily fulfill the high demand of AI developers for quality data. This study is expected to help other researchers intending to generate similar platforms that are more effective and accessible in the future.

PAIP 2019: Liver cancer segmentation challenge.

Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater accessibility. The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. The goal of the challenge was to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). Additionally, the PAIP of this year attempted to address potential future problems of AI applicability in clinical settings. In the challenge, participants were asked to use analytical data and statistical metrics to evaluate the performance of automated algorithms in two different tasks. The participants were given the two different tasks: Task 1 involved investigating Liver Cancer Segmentation and Task 2 involved investigating Viable Tumor Burden Estimation. There was a strong correlation between high performance of teams on both tasks, in which teams that performed well on Task 1 also performed well on Task 2. After evaluation, we summarized the top 11 team's algorithms. We then gave pathological implications on the easily predicted images for cancer segmentation and the challenging images for viable tumor burden estimation. Out of the 231 participants of the PAIP challenge datasets, a total of 64 were submitted from 28 team participants. The submitted algorithms predicted the automatic segmentation on the liver cancer with WSIs to an accuracy of a score estimation of 0.78. The PAIP challenge was created in an effort to combat the lack of research that has been done to address Liver cancer using digital pathology. It remains unclear of how the applicability of AI algorithms created during the challenge can affect clinical diagnoses. However, the results of this dataset and evaluation metric provided has the potential to aid the development and benchmarking of cancer diagnosis and segmentation.