Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

This study aimed to examine the effects of creativity-enhanced tasks on middle school students’ English performance, creativity development, and perceptions of creativity and affective factors including motivation and attitudes toward English learning. The participants were 49 middle school students in Seoul, Korea. The experimental group was treated with creativity-enhanced tasks whereas the control group received traditional English instruction. The overall study was carried out for fourteen weeks. The main findings are summarized as the following. First, there were no significant differences found between the experimental and the control group in terms of language performance. Second, the experimental group indicated substantial improvement in creativity, especially regarding fluency and originality. Finally, the results revealed positive changes in students’ intrinsic and extrinsic motivation, and attitudes toward English learning. The findings of this study are expected to provide practical insights to English teachers and educators who hope to foster creativity in students by supporting a creativity-enhanced language classroom.

During the decommissioning of nuclear facilities, 3D digital model that precisely describes the work environment can expedite the accomplishment of the work. Thus, the workers’ exposure to radiation is minimized and the safety risk to the workers is reduced, while precluding inadvertent effects on the environment. However, it is common that the 3D model does not exist for legacy nuclear facilities as most of the initial design drawings are 2D drawings and even some of the 2D drawings are missing. Even in the case that all of the 2D drawings are intact, these initial design drawings need to be updated using asbuilt data because facilities get modified through years of operation. In those cases, 3D scanning can be a good option to quickly and accurately generate a structure’s actual 3D geometric information. 3D scanning is a technique used to capture the shape of an object in the form of point cloud. Point cloud is a collection of large number of points on the external surfaces of objects measured by 3D scanners. The conversion of point cloud to 3D digital model is a labor-intensive process as a human worker needs to recognize objects in the point cloud and convert the objects into 3D model, even though some of the conversion process can be automated by using commercial software packages. With the aim of full automation of scan-to-3D-model process, deep learning techniques that take point cloud as input and generate corresponding 3D model have been studies recently. This paper introduces an efficient scan simulation method. The simulator generates synthetic point cloud data used to train deep learning models for classifying reactor parts in robotic nuclear decommissioning system. The simulator is built by implementing a ray-casting mechanism using a python library called ‘Pycaster’. In order to improve the speed of simulation, multiprocessing is applied. This paper describes the ray casting simulation mechanism and compares the in-house scan simulator with an open source sensor simulation package called Blensor.

The purpose of the present research is to verify the design characteristics of the SMART facility for the application of the IAEA’s safeguards-by-design (SBD) concept to small modular reactor (SMR) and to establish a foundation for SBD to be faithfully implemented as early as possible from the design stage. International Atomic Energy Agency (IAEA) is planning to facilitate the verification activities of inspectors by developing a safeguards approach to the reactor as early as possible and preparing a safeguards technical report (STR) before commercial operation of SMR begins. To this end, the IAEA is developing various approaches to the application of SBD to SMR with countries such as Republic of Korea, Russian Federation, China, the United States, and Canada through the Member State Support Program (MSSP). In order to review the unique design information of SMART facilities, the only deployable SMR in Korea, and to establish safeguards from the early design stages of SMART, it is necessary to carry out the task through cooperation with the Korea Atomic Energy Research Institute (KAERI) and Korea Institute of Nuclear Nonproliferation and Control (KINAC). IAEA agreed with the KINAC and KAERI to the direction of the project and to prepare both the Design Information Questionnaire (DIQ) and the Safeguards Technical Report (STR) for SMART facilities sequentially. The DIQ is a collection of questions to understand the characteristics of the reactor facilities that must be considered in applying safeguards. The STR is a document referenced by IAEA inspectors when verifying safeguards. Those draft versions were prepared and submitted to the IAEA. After review opinions were received, additional revision was conducted. In 2022, the IAEA holds the consultancy meeting on SBD for SMART. The purpose of the meeting is to review the draft DIQ and STR prepared by designers and discuss the future work plan of the task with designer and the task point of contact in order to safeguards can be considered at the early stage of the design. The results will be beneficial to the efficient safeguards verification activities of IAEA inspectors in the future.