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This study aims to develop PM2.5 prediction models using air pollutant data (PM10, NO2, SO2, O3, CO, and PM2.5) and meteorological data (temperature, humidity, wind speed, atmospheric pressure, precipitation, and snowfall) measured in South Korea from 2015 to 2019. Two prediction models were developed using an artificial neural network (ANN): a nationwide (NW) model and administrative districts (AD) model. To develop the prediction models, the independent variables daily averages and variances of air pollutant data and meteorological data (independent variables) were used as independent variables, and daily average PM2.5 concentration set as a dependent variable. First, the correlations between independent and dependent variables were analyzed. Second, prediction models were developed using an ANN to predict next-day PM2.5 daily average concentration, both NW and in 16 AD. The ANN models were optimized using a factorial design to determine the hidden layer layout and threshold, and a seasonal (monthly) factor was also considered. In the optimal prediction model, the absolute error in 1 σ was 91% (in-sample 91%, out-of-sample 91%) for the NW model, and the absolute error in 1 σ was 86% (in-sample 88%, out-of-sample 84%) for AD model. The accuracy of these prediction models increases further when they are developed using the next-day weather data, assuming that the weather prediction is accurate. [ABSTRACT FROM AUTHOR]

Truck platooning has recently become an essential issue in automatic driving. Though truck platooning can increase safety and reduce fuel consumption and carbon emissions, the practical vehicle routing problem involved in truck platooning has not been sufficiently addressed. Therefore, we design a mixed-integer linear programming model for the routing problem in truck platooning considering the deadline of vehicles, continuous-time units, different fuel reduction rates, traffic congestion avoidance, and heterogeneous vehicles. In addition, a forward–backward heuristic called the "greedy heuristic" is presented for reasonable computation time. To validate the model's performance, several parameters, such as the percentage of fuel reduction, percentage of detour vehicles, and percentage of platooned links (road segments), are considered. Additionally, various cases are considered with varying fuel reduction rates, traffic flow rates, and time windows. [ABSTRACT FROM AUTHOR]

With the spread of COVID-19, the "untact" culture in South Korea is expanding and customers are increasingly seeking for online services. A recommendation system serves as a decision-making indicator that helps users by suggesting items to be purchased in the future by exploring the symmetry between multiple user activity characteristics. A plethora of approaches are employed by the scientific community to design recommendation systems, including collaborative filtering, stereotyping, and content-based filtering, etc. The current paradigm of recommendation systems favors collaborative filtering due to its significant potential to closely capture the interest of a user as compared to other approaches. The collaborative filtering harnesses features like user-profile details, visited pages, and click information to determine the interest of a user, thereby recommending the items that are related to the user's interest. The existing collaborative filtering approaches exploit implicit and explicit features and report either good classification or prediction outcome. These systems fail to exhibit good results for both measures at the same time. We believe that avoiding the recommendation of those items that have already been purchased could contribute to overcoming the said issue. In this study, we present a collaborative filtering-based algorithm to tackle big data of user with symmetric purchasing order and repetitive purchased products. The proposed algorithm relies on combining extreme gradient boosting machine learning architecture with word2vec mechanism to explore the purchased products based on the click patterns of users. Our algorithm improves the accuracy of predicting the relevant products to be recommended to the customers that are likely to be bought. The results are evaluated on the dataset that contains click-based features of users from an online shopping mall in Jeju Island, South Korea. We have evaluated Mean Absolute Error, Mean Square Error, and Root Mean Square Error for our proposed methodology and also other machine learning algorithms. Our proposed model generated the least error rate and enhanced the prediction accuracy of the recommendation system compared to other traditional approaches. [ABSTRACT FROM AUTHOR]

To construct a polyetherimide (PEI)-reinforced polyvinylidene fluoride (PVdF) composite membrane with multicore-shell structure, a ternary solution was prepared and electrospun by single-nozzle electrospinning. A theoretical prediction was made for the feasibility of complete distinction of two phases. The diameters of the membrane fibers and the PEI multi-core fibrils varied with the PEI ratio and the spinning time, respectively. The tensile strength and modulus were improved to 48 MPa and 1.5 GPa, respectively. The shrinkage of the membrane was only 6.6% at 180 °C, at which temperature the commercial PE separator melted down. The reinforcement in mechanical and thermal properties is associated with multiple PEI nanofibrils oriented along the fiber axis. Indeed, the unique morphology of self-assembled multicore-shell fibers plays an important role in their properties. All in all, PEI/PVdF membranes are appropriate for a lithium-ion battery application due to their high mechanical strength, excellent thermal stability, and controllable textural properties. [ABSTRACT FROM AUTHOR]