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In modern times, people predominantly use personal vehicles as a means of transportation, and, as this trend has developed, services that enable consumers to rent vehicles instead of buying their own have emerged. These services have grown into an industry, and the demand for predicting rental prices has arisen with the number of consumers. This study addresses the challenge in accurately predicting rental prices using big data with numerous features, and presents the experiments conducted and results obtained by applying various machine learning (ML) algorithms to enhance the prediction accuracy. Our experiment was conducted in two parts: single- and multi-step forecasting. In the single-step forecasting experiment, we employed random forest regression (RFR), multilayer perceptron (MLP), 1D convolutional neural network (1D-CNN), long short-term memory (LSTM), and the autoregressive integrated moving average (ARIMA) model to predict car rental prices and compared the results of each model. In the multi-step forecasting experiment, rental prices after 7, 14, 21 and 30 days were predicted using the algorithms applied in single-step forecasting. The prediction performance was improved by applying Bayesian optimization hyperband. The experimental results demonstrate that the LSTM and ARIMA models were effective in predicting car rental prices. Based on these results, useful information could be provided to both rental car companies and consumers. [ABSTRACT FROM AUTHOR]

This paper presents a direct 3D numerical simulation of biaxial surface wrinkling of thin metal film on a compliant substrate. The selected compliant substrate is a commercial Scotch tape on which a gold metal thin film has been transferred by using low adhesion between the thin metal film and polyimide substrate. Compared with the previous fabrication of a cylindrical thin-film wrinkling pattern, an undulated wrinkling pattern has been implemented by increasing the width of the thin metal film in order to create biaxial straining in the thin film. To understand the wrinkling behavior due to biaxial loading, a simple direct numerical simulation based on material imperfections defined in the compliant substrate has been conducted. Through modeling and simulation, it was found that the wrinkling mode is determined by the biaxiality ratio (BR), the ratio between transversal strain and longitudinal strain. Depending on the BR, the wrinkling mode belongs to one of the cylindrical, undulated (or herringbone), checkerboard, or labyrinth modes as a function of applied strain. The cylindrical wrinkling is dominant at the input of BR less than 0.5, while the undulated (or herringbone) ones become dominant just after the onset of the wrinkling pattern at BR greater than 0.9. Through the comparison of the wrinkling patterns between simulation and experiment, the applied BR of the fabricated thin film has been successfully estimated. [ABSTRACT FROM AUTHOR]

This study presents a development plan for a vision AI system to enhance productivity in industrial environments, where environmental control is challenging, by using AI technology. An image pre-processing algorithm was developed using a mobile robot that can operate in complex environments alongside workers to obtain high-quality learning and inspection images. Additionally, the proposed architecture for sustainable AI system development included cropping the inspection part images to minimize the technology development time, investment costs, and the reuse of images. The algorithm was retrained using mixed learning data to maintain and improve its performance in industrial fields. This AI system development architecture effectively addresses the challenges faced in applying AI technology at industrial sites and was demonstrated through experimentation and application. [ABSTRACT FROM AUTHOR]

The MIXv2 Asian emission inventory is developed under the framework of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase IV and produced from a mosaic of up-to-date regional emission inventories. We estimated the emissions for anthropogenic and biomass burning sources covering 23 countries and regions in East, Southeast and South Asia and aggregated emissions to a uniform spatial and temporal resolution for seven sectors: power, industry, residential, transportation, agriculture, open biomass burning and shipping. Compared to MIXv1, we extended the dataset to 2010–2017, included emissions of open biomass burning and shipping, and provided model-ready emissions of SAPRC99, SAPRC07, and CB05. A series of unit-based point source information was incorporated covering power plants in China and India. A consistent speciation framework for non-methane volatile organic compounds (NMVOCs) was applied to develop emissions by three chemical mechanisms. The total Asian emissions for anthropogenic/open biomass sectors in 2017 are estimated as follows: 41.6/1.1 Tg NO x , 33.2/0.1 Tg SO 2 , 258.2/20.6 Tg CO, 61.8/8.2 Tg NMVOC, 28.3/0.3 Tg NH 3 , 24.0/2.6 Tg PM 10 , 16.7/2.0 Tg PM 2.5 , 2.7/0.1 Tg BC (black carbon), 5.3/0.9 Tg OC (organic carbon), and 18.0/0.4 Pg CO 2. The contributions of India and Southeast Asia were emerging in Asia during 2010–2017, especially for SO 2 , NH 3 and particulate matter. Gridded emissions at a spatial resolution of 0.1° with monthly variations are now publicly available. This updated long-term emission mosaic inventory is ready to facilitate air quality and climate model simulations, as well as policymaking and associated analyses. [ABSTRACT FROM AUTHOR]

The MIXv2 Asian emission inventory is developed under the framework of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase IV, and produced from a mosaic of up-to-date regional emission inventories. We estimated the emissions for anthropogenic and biomass burning sources covering 23 countries and regions in East, Southeast and South Asia, and aggregated emissions to a uniform spatial and temporal resolution for seven sectors: power, industry, residential, transportation, agriculture, open biomass burning and shipping. Compared to MIXv1, we extended the dataset to 2010 – 2017, included emissions of open biomass burning and shipping, and provided model-ready emissions of SAPRC99, SAPRC07, and CB05. A series of unit-based point source information was incorporated covering power plants in China and India. A consistent speciation framework for Non-Methane Volatile Organic Compounds (NMVOCs) was applied to develop emissions by three chemical mechanisms. The total Asian emissions for anthropogenic | open biomass sectors in 2017 are estimated as follows: 41.6 | 1.1 Tg NO x , 33.2 | 0.1 Tg SO2, 258.2 | 20.6 Tg CO, 61.8 | 8.2 Tg NMVOC, 28.3 | 0.3 Tg NH3, 24.0 | 2.6 Tg PM10, 16.7 | 2.0 Tg PM2.5, 2.7 | 0.1 Tg BC, 5.3 | 0.9 Tg OC, and 18.0 | 0.4 Pg CO2. The contributions of India and Southeast Asia have been emerging in Asia during 2010–2017, especially for SO2, NH3 and particulate matters. Gridded emissions at a spatial resolution of 0.1 degree with monthly variations are now publicly available at https://csl.noaa.gov/groups/csl4/modeldata/data/Li2023/. [ABSTRACT FROM AUTHOR]