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Adolescence is a vulnerable period for psychological distress. This study aimed to comprehensively investigate the distribution of major stress-causing factors and their correlates in a large population of adolescents. A computerized self-report questionnaire was completed by 291,110 participating adolescents over a 4-year period using a cross-sectional study design. The questionnaire included items assessing demographic variables, major stressors, severity of perceived stress, and mental health outcomes such as depressed mood, suicidal ideation, and attempts. Among the major stressors, schoolwork/career was the most predominant (54.7%). However, the odds ratios for depressed mood and suicidal ideation were the highest in the stressor of conflict with peers (OR = 1.97 for depressed mood and OR = 2.00 for suicidal ideation), followed by family circumstances (OR = 1.77 and 1.94 for depressed mood and suicidal ideation, respectively). Conversely, schoolwork/career presented significantly lower odds ratios of 0.87 and 0.87 for depressed mood and suicidal ideation, respectively. This study provides important information on psychological distress related to adolescents’ mental health. Although school/career was the most prevalent source of psychological stress, the odds ratios for negative health outcomes of adolescents were higher in interpersonal problems such as conflicts with parents and peers and family circumstances. The present findings might help parents and clinicians understand the psychological distress of adolescents and improve their mental health.

The bidirectional reflectance distribution function (BRDF) is important for estimating the physical properties of a surface in remote sensing. In the laboratory, the BRDF can be estimated quickly and accurately using a goniometer, but it is very difficult to operate in the field. The purpose of this study was to evaluate whether estimating the BRDF with reasonable accuracy using an unmanned aerial vehicle (UAV) with a multispectral camera is possible in the field. Hemispherical reflectance was created from images taken using an UAV multispectral camera. The ground targets were four calibrated reference tarps (CRTs) of different reflectance, and the UAV was operated five times. Down-welling irradiance for reflectance calculation was measured in two ways: a sunlight sensor was mounted on a UAV, and a spectroradiometer with a remote cosine receptor (RCR) was installed on the ground. The BRDF was assessed through the anisotropy factor (ANIF) of the CRT reflectance derived from the collected data. As a result, the irradiance data for the reflectance calculation were more effective from the spectroradiometer with RCR on the ground than from the sunlight sensor mounted on an UAV. Furthermore, the high reflectance CRTs, ANIF, and BRDF had similar results. Therefore, when analyzing the BRDF, the effectiveness can be guaranteed when the reflectance of the target is over 21~46%, because a low reflectance tendency differs due to the adjacency effect. In addition, weather affects irradiance, so it is more effective to conduct fieldwork in clear weather.

Several studies have reported an increasing trend of surface ozone in South Korea over the past few decades, using different measurement metrics. In this study, we examined the surface ozone trends in South Korea by analyzing the hourly or daily maximum 8 h average ozone concentrations (MDA8) measured at the surface from 2001 to 2021. We studied the diurnal, seasonal, and multi-decadal variations of these parameters at city, province, and background sites. We found that the fourth-highest MDA8 values exhibited positive trends in seven cities, nine provinces, and two background sites from 2001 to 2021. For the majority of sites, there was an annual increase of approximately 1–2 ppb. After early 2010, all sites consistently recorded MDA8 values exceeding 70 ppb, despite reductions in precursor pollutants such as NO2 and CO. The diurnal and seasonal characteristics of ozone exceedances, defined as the percentage of data points with hourly ozone concentrations exceeding 70 ppb, differed between the Seoul Metropolitan Area (SMA) and the background sites. In the SMA, the exceedances were more prevalent during summer compared to spring, whereas the background sites experienced higher exceedances in spring than in summer. This indicates the efficient local production of ozone in the SMA during summer and the strong influence of long-range transport during spring. The rest of the sites showed similar exceedance patterns during both spring and summer. The peak exceedances occurred around 16:00–17:00 in the SMA and most locations, while the background sites primarily recorded exceedances throughout the night. During the spring of the COVID-19 pandemic (2020–2021), ozone exceedances decreased at most locations, potentially due to significant reductions in NOx emissions in South Korea and China compared to the period of 2010–2019. The largest decreases in exceedances were observed at the background sites during spring. For instance, in Gosung, Gangwondo (approximately 600 m above sea level), the exceedances dropped from 30 % to around 5 % during the COVID-19 pandemic. Regional model simulations confirmed the concept of decreased ozone levels in the boundary layer in Seoul and Gangwon-do in response to emission reductions. However, these reductions in ozone exceedances were not observed in major cities and provinces during the summer of the COVID-19 pandemic, as the decreases in NOx emissions in South Korea and China were much smaller compared to spring. This study highlights the distinctions between spring and summer in the formation and transport of surface ozone in South Korea, emphasizing the importance of monitoring and modeling specific processes for each season or finer timescales.

Febrile neutropenia (FN) is one of the most concerning complications of chemotherapy, and its prediction remains difficult. This study aimed to reveal the risk factors for and build the prediction models of FN using machine learning algorithms. Medical records of hospitalized patients who underwent chemotherapy after surgery for breast cancer between May 2002 and September 2018 were selectively reviewed for development of models. Demographic, clinical, pathological, and therapeutic data were analyzed to identify risk factors for FN. Using machine learning algorithms, prediction models were developed and evaluated for performance. Of 933 selected inpatients with a mean age of 51.8 ± 10.7 years, FN developed in 409 (43.8%) patients. There was a significant difference in FN incidence according to age, staging, taxane-based regimen, and blood count 5 days after chemotherapy. The area under the curve (AUC) built based on these findings was 0.870 on the basis of logistic regression. The AUC improved by machine learning was 0.908. Machine learning improves the prediction of FN in patients undergoing chemotherapy for breast cancer compared to the conventional statistical model. In these high-risk patients, primary prophylaxis with granulocyte colony-stimulating factor could be considered.

Remote sensing (RS) has been used to monitor inaccessible regions. It is considered a useful technique for deriving important environmental information from inaccessible regions, especially North Korea. In this study, we aim to develop a tree species classification model based on RS and machine learning techniques, which can be utilized for classification in North Korea. Two study sites were chosen, the Korea National Arboretum (KNA) in South Korea and Mt. Baekdu (MTB; a.k.a., Mt. Changbai in Chinese) in China, located in the border area between North Korea and China, and tree species classifications were examined in both regions. As a preliminary step in developing a classification algorithm that can be applied in North Korea, common coniferous species at both study sites, Korean pine (Pinus koraiensis) and Japanese larch (Larix kaempferi), were chosen as targets for investigation. Hyperion data have been used for tree species classification due to the abundant spectral information acquired from across more than 200 spectral bands (i.e., hyperspectral satellite data). However, it is impossible to acquire recent Hyperion data because the satellite ceased operation in 2017. Recently, Sentinel-2 satellite multispectral imagery has been used in tree species classification. Thus, it is necessary to compare these two kinds of satellite data to determine the possibility of reliably classifying species. Therefore, Hyperion and Sentinel-2 data were employed, along with machine learning techniques, such as random forests (RFs) and support vector machines (SVMs), to classify tree species. Three questions were answered, showing that: (1) RF and SVM are well established in the hyperspectral imagery for tree species classification, (2) Sentinel-2 data can be used to classify tree species with RF and SVM algorithms instead of Hyperion data, and (3) training data that were built in the KNA cannot be used for the tree classification of MTB. Random forests and SVMs showed overall accuracies of 0.60 and 0.51 and kappa values of 0.20 and 0.00, respectively. Moreover, combined training data from the KNA and MTB showed high classification accuracies in both regions; RF and SVM values exhibited accuracies of 0.99 and 0.97 and kappa values of 0.98 and 0.95, respectively.

With the development of technology, wireless and IoT devices are increasingly used from daily life to industry, placing demands on rapid and efficient manufacturing processes. This study demonstrates the fabrication of an IoT device using a roll-to-roll printing process, which could shorten the device fabrication time and reduce the cost of mass production. Here, the fabricated IoT device is designed to acquire data through the sensor, process the data, and communicate with end-user devices via Bluetooth communication. For fabrication, a four-layer circuit platform consisting of two conductive layers, an insulating layer including through holes, and a solder resist layer is directly printed using a roll-to-roll screen printing method. After the printing of the circuit platform, an additional layer of solder paste is printed to assemble the electrical components into the device, inspiring the fully roll-to-roll process for device fabrication. Successful IoT device deployment opens the chance to broaden the roll-to-roll fabrication process to other flexible and multilayer electronic applications.

Skin photoaging induced by ultraviolet (UV) irradiation contributes to the formation of thick and coarse wrinkles. Humans are exposed to UV light throughout their lives. Therefore, it is crucial to determine the time-sequential effects of UV on the skin. In this study, we irradiated the mouse back skin with UV light for eight weeks and observed the changes in gene expressions via microarray analysis every week. There were more downregulated genes (514) than upregulated genes (123). The downregulated genes had more functional diversity than the upregulated genes. Additionally, the number of downregulated genes did not increase in a time-dependent manner. Instead, time-dependent kinetic patterns were observed. Interestingly, each kinetic cluster harbored functionally enriched gene sets. Since collagen changes in the dermis are considered to be a major cause of photoaging, we hypothesized that other gene sets contributing to photoaging would exhibit kinetics similar to those of the collagen-regulatory genes identified in this study. Accordingly, co-expression network analysis was conducted using 11 well-known collagen-regulatory seed genes to predict genes with similar kinetics. We ranked all downregulated genes from 1 to 504 based on their expression levels, and the top 50 genes were suggested to be involved in the photoaging process. Additionally, to validate and support our identified top 50 gene lists, we demonstrated that the genes ( FN1 , CCDC80 , PRELP , and TGFBR3 ) we discovered are downregulated by UV irradiation in cultured human fibroblasts, leading to decreased collagen levels, which is indicative of photoaging processes. Overall, this study demonstrated the time-sequential genetic changes in chronically UV-irradiated skin and proposed 50 genes that are involved in the mechanisms of photoaging.

An attempt was made to control the surface charge of colloidal silica nanoparticles with 20 nm and 100 nm diameters. Untreated silica nanoparticles were determined to be highly negatively charged and have stable hydrodynamic sizes in a wide pH range. To change the surface to a positively charged form, various coating agents, such as amine containing molecules, multivalent metal cation, or amino acids, were used to treat the colloidal silica nanoparticles. Molecules with chelating amine sites were determined to have high affinity with the silica surface to make agglomerations or gel-like networks. Amino acid coatings resulted in relatively stable silica colloids with a modified surface charge. Three amino acid moiety coatings (L-serine, L-histidine, and L-arginine) exhibited surface charge modifying efficacy of L-histidine > L-arginine > L-serine and hydrodynamic size preservation efficacy of L-serine > L-arginine > L-histidine. The time dependent change in L-arginine coated colloidal silica was investigated by measuring the pattern of the backscattered light in a Turbiscan™. The results indicated that both the 20 nm and 100 nm L-arginine coated silica samples were fairly stable in terms of colloidal homogeneity, showing only slight coalescence and sedimentation.

The most recent forest-type map of the Korean Peninsula was produced in 1910. That of South Korea alone was produced since 1972; however, the forest type information of North Korea, which is an inaccessible region, is not known due to the separation after the Korean War. In this study, we developed a model to classify the five dominant tree species in North Korea (Korean red pine, Korean pine, Japanese larch, needle fir, and Oak) using satellite data and machine-learning techniques. The model was applied to the Gwangneung Forest area in South Korea; the Mt. Baekdu area of China, which borders North Korea; and to Goseong-gun, at the border of South Korea and North Korea, to evaluate the model’s applicability to North Korea. Eighty-three percent accuracy was achieved in the classification of the Gwangneung Forest area. In classifying forest types in the Mt. Baekdu area and Goseong-gun, even higher accuracies of 91% and 90% were achieved, respectively. These results confirm the model’s regional applicability. To expand the model for application to North Korea, a new model was developed by integrating training data from the three study areas. The integrated model’s classification of forest types in Goseong-gun (South Korea) was relatively accurate (80%); thus, the model was utilized to produce a map of the predicted dominant tree species in Goseong-gun (North Korea).

A Kekulé lattice is an exotic, distorted lattice structure exhibiting alternating bond lengths, distinguished from naturally formed atomic crystals. Despite its evident applicability, the formation of a Kekulé lattice from topological solitons in magnetic systems has remained elusive. Here, we propose twisted bilayer easy-plane Néel antiferromagnets as a promising platform for achieving a “Meron Kekulé lattice”—a distorted topological soliton lattice comprised of antiferromagnetic merons as its lattice elements. We demonstrate that the cores of these merons are stabilized into the Kekulé-O pattern with different intracell and intercell bond lengths across moiré supercells, thereby forming a Meron Kekulé lattice. Moreover, the two bond lengths of the Meron Kekulé lattice can be fine-tuned by adjusting the twist angle and specifics of the interlayer exchange coupling, suggesting extensive control over the meron lattice configuration in contrast to conventional magnetic systems. These discoveries pave the way for exploring topological solitons with distinctive Kekulé attributes.