In the event of an contingency situation of physical protection in nuclear facilities, the first organization to deal with at the forefront is the Special Response Forces (SRF). Since the SRF has to perform nuclear facility protection at the actual battle site, they must repeatedly train tactical understanding such as shooting, entry, and suppression so that their body can remember it even in real contingency situations (called Muscle Memory). In reality, however, repeated training using firearms is very difficult due to high risk and high cost, except for some military and police organizations. Using the advantages of VR technology, the Korea Institute of Nuclear nonproliferation and control (KINAC) has developed educational contents of “VR Shooting Training Center (VR STC)” to enable low-risk, low-cost, and repeated shooting training for these high-risk, high-cost training. This content was developed by dividing it into an “indoor” and “outdoor” training field. Educational firearms are all developed as gas guns to add to the sense of reality, and trainees can choose firearms, distance movement of targets and other options. The “Indoor training field” was developed by imitating an actual indoor shooting field, in particular the “outdoor training field” was developed using VR technology and motion tracking technology. Therefore, in “outdoor training field”, trainees can move freely within the designated spot of not only in VR content but also reality and then have to perform some missions. Trainees have to overcome random obstacles as they move to a designated destination, and at the destination, they are attacked by terrorists. Therefore, trainees must complete missions by concealing their bodies using objects around them. The one training course includes a total of 10 missions, and after the training is completed, comprehensive training results such as shooting accuracy and mission completion are expressed. VR STC will be a representative example of making high-risk, high-cost training into low-risk, low-cost, and repeated training. In this respect, VR technology can be used to develop various radiation protection curriculums accompanied by high risk and high cost, and can improve educational effects.

In order to effectively and efficiently apply safeguards to new nuclear facilities, it is recommended to apply safeguards-by-design concept. In evaluating the safeguards in the early stage of the design of a facility, it is essential to analyze the diversion path for nuclear materials. This study suggests a simple method which can generate diversion paths. The essential components constituting the diversion path were reviewed and the logical flow for systematically creating the diversion path was developed. The path generation algorithm is based on this components and logical flow as well as the initial information of the nuclear materials and material flows in a planned facilities. The results will be used to develop a program module which can systematically generate diversion paths using the event tree and fault tree method.

Non-destructive estimation of leaf area is a more efficient and convenient method than leaf excision. Thus, several models predicting leaf area have been developed for various horticultural crops. However, there are limited studies on estimating the leaf area of strawberry plants. In this study, we predicted the leaf areas via nonlinear regression analysis using the leaf lengths and widths of three-compound leaves in five domestic strawberry cultivars (‘Arihyang’, ‘Jukhyang’, ‘Keumsil’, ‘Maehyang’, and ‘Seollhyang’). The coefficient of determination (R2) between the actual and estimated leaf areas varied from 0.923 to 0.973. The R2 value varied for each cultivar; thus, leaf area estimation models must be developed for each cultivar. The leaf areas of the three cultivars ‘Jukhyang’, ‘Seolhyang’, and ‘Maehyang’ could be non-destructively predicted using the model developed in this study, as they had R2 values over 0.96. The cultivars ‘Arihyang’ and ‘Geumsil’ had slightly low R2 values, 0.938 and 0.923, respectively. The leaf area estimation model for each cultivar was coded in Python and is provided in this manuscript. The estimation models developed in this study could be used extensively in other strawberry-related studies.