Jin-Soo Kim’s research while affiliated with Kangwon National University and other places

Objectives This study aimed to establish updated diagnostic reference levels (DRLs) for dental imaging modalities in South Korea. Methods In cooperation with the Korea Disease Control and Prevention Agency, various types of institutions (dental clinics, dental hospitals, and dental university hospitals) were selected to investigate the status of diagnostic radiation equipment use. Subsequently, over 300 units were randomly selected for each imaging device type (intraoral, panoramic, and cone-beam CT [CBCT]) as measurement samples. DRLs were defined as the 75th percentile of the dose area product distribution. The differences in dose were analysed based on the type of institution, age of use, country of manufacture, and presence of a multifunction device. Results The national DRLs for dental imaging established in this survey were as follows: intraoral imaging at 48 mGy·cm2 for adults and 31 mGy·cm2 for children; panoramic imaging at 354 mGy·cm2 for adults and 224 mGy·cm2 for children; and CBCT at 1856 mGy·cm2 for adults and 1350 mGy·cm2 for children. Private dental clinics and hospitals recorded approximately twice the dose levels of university dental hospitals. CBCT devices in dental hospitals and those that have been in used for 5-10 years showed significantly high radiation doses. Conclusions The DRLs established through this study were found to be significantly increased, especially in adult and paediatric panoramic radiographs and paediatric CBCT images, compared with those in previous surveys; moreover, they were higher than those in other countries. The findings of this study can serve as a basis for national dose reduction efforts.

The escalating demand for pork highlights the importance of swift and accurate pregnancy diagnosis in sows, a crucial factor in farm profitability. The prevalent use of low-frequency ultrasound devices in this context poses a challenge owing to the suboptimal resolution of the resultant images. This study introduces an innovative approach for sow pregnancy diagnosis using deep learning techniques to analyze low-frequency ultrasound images. Our methodology encompasses the development and comparative analysis of three distinct classification models: ViT-H, ConvNeXt-xlarge, and Xception. These models aim to improve diagnostic accuracy. AutoAugment was used to augment the data to expand the training dataset, thereby enhancing the robustness of the models under varied conditions. Results indicate a notable improvement in diagnostic performance, with the implementation of AutoAugment leading to significant achievements in the models, reflected by AUC values of 0.865, 0.856, and 0.866. These outcomes affirm the viability of deep learning in the effective management of sow pregnancies in livestock farms and suggest potential applications in broader animal husbandry contexts. This research marks a significant contribution to the evolution of agricultural technologies, presenting a scalable and efficacious solution for sow pregnancy diagnosis.

Cholesterol granuloma is known to be a disease formed by hemosiderin and cholesterol crystals when bleeding occurs while ventilation and drainage are not well performed by inflammatory tissue. It is known to occur mainly inside the temporal bone that has been undergone pneumatization, and occurrences in the paranasal sinuses have been rarely reported. A 72-year-old female patient visited to our institution, complaining of a sensation that pus had been coming out of her upper right extraction sockets for the past few months ago. She underwent the extraction of her upper right first and second molars a year ago. In the CBCT image, a well-circumscribed and expansile cystic lesion was located in the right maxillary sinus, bulging posterolateral wall of the maxillary sinus, with amorphous internal calcification and sclerotic border. After surgical excision, the lesion was confirmed as cholesterol granuloma with massive ossification by the histopathological interpretation postoperatively.

The vast compositional and configurational spaces of multi-element metal halide perovskites (MHPs) result in significant challenges when designing MHPs with promising stability and optoelectronic properties. In this paper, we propose a framework for the design of B-site-alloyed ABX 3 MHPs by combining density functional theory (DFT) and machine learning (ML). We performed generalized gradient approximation with Perdew–Burke–Ernzerhof functional for solids (PBEsol) on 3,159 B-site-alloyed perovskite structures using a compositional step of 1/4. Crystal graph convolution neural networks (CGCNNs) were trained on the 3159 DFT datasets to predict the decomposition energy, bandgap, and types of bandgaps. The trained CGCNN models were used to explore the compositional and configurational spaces of 41,400 B-site-alloyed ABX 3 MHPs with a compositional step of 1/16, by accessing all possible configurations for each composition. The electronic band structures of the selected compounds were calculated using the hybrid functional (PBE0). Then, we calculated the optical absorption spectra and spectroscopic limited maximum efficiency of the selected compounds. Based on the DFT/ML-combined screening, 10 promising compounds with optimal bandgaps were selected, and from among these 10 compounds, CsGe 0.3125 Sn 0.6875 I 3 and CsGe 0.0625 Pb 0.3125 Sn 0.625 Br 3 were suggested as photon absorbers for single-junction and tandem solar cells, respectively. The design framework presented herein is a good starting point for the design of mixed MHPs for optoelectronic applications.