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Background: This study aimed to identify the prevalence of workplace hazards and organizational protection resources according to the size of the enterprise in the manufacturing industry of the Republic of Korea. Methods: We analyzed data of waged workers (weighted N = 5,879) from the Fifth Korean Working Conditions Survey (2017). Enterprise sizes were categorized as “micro enterprises” (less than five employees), “small enterprises” (5–49 employees) and “medium-large enterprises” (50 or more employees). Self-reported exposure to 18 physical, chemical, ergonomic, and psychological hazards were measured. The presence of organizational protection resources such as a labor union, a safety delegate working at the company, designated spaces to deal with safety, and the provision of health and safety information was evaluated. Results: Compared to workers in medium-large enterprises, those in micro enterprises showed a higher proportion of exposure to most of physical, chemical, ergonomic, and psychological hazards, except for exposure to solvents, prolonged sitting, and experiencing a state of emotional unrest. On the other hand, workers in micro enterprises had the lowest proportion of access to organizational protection resources. Conclusion: Our study demonstrates that manufacturing workers at the micro enterprise in the Republic of Korea are exposed to the most hazardous work environment and yet have access to the fewest organizational protection resources.

Objectives: Electroacupuncture (EA) has been demonstrated to aid stroke recovery. However, few investigations have focused on identifying the potent molecular targets of EA by comparing EA stimulation between naïve and disease models. Therefore, this study was undertaken to identify the potent molecular therapeutic mechanisms underlying EA stimulation in ischemic stroke through a comparison of mRNA sequencing data obtained from EA-treated naïve control and ischemic stroke mouse models. Methods: Using both naïve control and middle cerebral artery occlusion (MCAO) mouse models, EA stimulation was administered at two acupoints, Baihui (GV20) and Dazhui (GV14), at a frequency of 2 Hz. Comprehensive assessments were conducted, including behavioral evaluations, RNA sequencing to identify differentially expressed genes (DEGs), functional enrichment analysis, protein-protein interaction (PPI) network analysis, and quantitative real-time PCR. Results: EA stimulation ameliorated the ischemic insult-induced motor dysfunction in mice with ischemic stroke. Comparative analysis between control vs. MCAO, control vs. control + EA, and MCAO vs. MCAO + EA revealed 4,407, 101, and 82 DEGs, respectively. Of these, 30, 7, and 1 were common across the respective groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed upregulated DEGs associated with the regulation of inflammatory immune response in the MCAO vs. MCAO + EA comparison. Conversely, downregulated DEGs in the control vs. control + EA comparison were linked to neuronal development. PPI analysis revealed major clustering related to the regulation of cytokines, such as Cxcl9, Pcp2, Ccl11, and Cxcl13, in the common DEGs of MCAO vs. MCAO + EA, with Esp8l1 identified as the only common downregulated DEG in both EA-treated naïve and ischemic models. Conclusion: These findings underscore the diverse potent mechanisms of EA stimulation between naïve and ischemic stroke mice, albeit with few overlaps. However, the potent mechanisms underlying EA treatment in ischemic stroke models were associated with the regulation of inflammatory processes involving cytokines.

Abstract Background In South Korea, Korean Chinese workers experience ethnic discrimination although they share physical similarities and ethnic heritage with native-born Koreans. This study aimed to examine whether perceived ethnic discrimination is associated with poor self-rated health and whether the association differs by gender among Korean Chinese waged workers in South Korea. Methods We conducted a pooled cross-sectional analysis using data of 13,443 Korean Chinese waged workers from the Survey on Immigrants’ Living Conditions and Labor Force conducted in 2018, 2020, and 2022. Based on perceived ethnic discrimination, asking for fair treatment, and subsequent situational improvement, respondents were classified into the following four groups: “Not experienced,” “Experienced, not asked for fair treatment,” “Experienced, asked for fair treatment, not improved,” and “Experienced, asked for fair treatment, improved.” Poor self-rated health was assessed using a single question “How is your current overall health?” We applied logistic regression to examine the association between perceived ethnic discrimination and poor self-rated health, with gender-stratified analyses. Results We found an association between ethnic discrimination and poor self-rated health among Korean Chinese waged workers. In the gender-stratified analysis, the “Experienced, not asked for fair treatment” group was more likely to report poor self-rated health compared to the “Not experienced” group, regardless of gender. However, gender differences were observed in the group stratified by situational improvements. For male workers, no statistically significant association was found in the “Experienced, asked for fair treatment, improved” group with poor self-rated health (odd ratios: 0.87, 95% confidence intervals: 0.30–2.53). Conversely, among female workers, a statistically significant association was observed (odd ratios: 2.63, 95% confidence intervals: 1.29–5.38). Conclusions This study is the first to find an association between perceived ethnic discrimination and poor self-rated health, along with gender differences in the association between situational improvements after asking for fair treatment and poor self-rated health among Korean Chinese waged workers in South Korea.

Oxidative stress, a driver of liver pathology, remains a challenge in clinical management, necessitating innovative approaches. In this research, we delved into the therapeutic potential of polyphenols for oxidative liver injury using a multiscale network analysis framework. From the Phenol-Explorer database, we curated a list of polyphenols along with their corresponding PubChem IDs. Verified target information was then collated from multiple databases. We subsequently measured the propagative effects of these compounds and prioritized a ranking based on their correlation scores for oxidative liver injury. This result underwent evaluation to discern its effectiveness in differentiating between known and unknown polyphenols, demonstrating superior performance over chance level in distinguishing these compounds. We found that lariciresinol and isopimpinellin yielded high correlation scores in relation to oxidative liver injury without reported evidence. By analyzing the impact on a multiscale network, we found that lariciresinol and isopimpinellin were predicted to offer beneficial effects on the disease by directly acting on targets such as CASP3, NR1I2, and CYP3A4 or by modulating biological functions related to the apoptotic process and oxidative stress. This study not only corroborates the efficacy of identified polyphenols in liver health but also opens avenues for future investigations into their mechanistic actions.

OBJECTIVE: This study aimed to assess the causal effect of a change in employment status on suicidal ideation and depressive symptoms by applying marginal structural models (MSM) with machine-learning (ML) algorithms. METHODS: We analyzed data from the 8–15^th waves (2013–2020) of the Korean Welfare Panel Study, a nationally representative longitudinal dataset. Our analysis included 13 294 observations from 3621 participants who had standard employment at baseline (2013–2019). Based on employment status at follow-up year (2014–2020), respondents were classified into two groups: (i) maintained standard employment (reference group), (ii) changed to non-standard employment. Suicidal ideation during the past year and depressive symptoms during the past week were assessed through self-report questionnaire. To apply the ML algorithms to the MSM, we conducted eight ML algorithms to build the propensity score indicating a change in employment status. Then, we applied the MSM to examine the causal effect by using inverse probability weights calculated based on the propensity score from ML algorithms. RESULTS: The random forest algorithm performed best among all algorithms, showing the highest area under the curve 0.702, 95% confidence interval (CI) 0.686–0.718. In the MSM with the random forest algorithm, workers who changed from standard to non-standard employment were 2.07 times more likely to report suicidal ideation compared to those who maintained standard employment (95% CI 1.16–3.70). A similar trend was observed in the analysis of depressive symptoms. CONCLUSIONS: This study found that a change in employment status could lead to a higher risk of suicidal ideation and depressive symptoms.

Abstract Galectin-3 levels have been studied as a potential biomarker for predicting cardiovascular (CV) risk and mortality in hemodialysis (HD) patients. Recently, a close relationship between galectin-3 and vascular calcification (VC) has been reported. Here, we investigated the role of VC as a mediating factor in the association between galectin-3 and mortality. Serum galectin-3 and baseline aortic arch calcification (AoAC) score were measured in 477 incident HD patients. Mortality data were obtained at a median follow-up of 40 months. Causal mediation analysis was performed to examine the effect of vascular risk factors on galectin-3-related mortality. The prevalence of AoAC in HD patients was 57% (n = 272), and elevated galectin-3 levels were associated with a significantly increased risk of AoAC. When the galectin-3 level was divided by the median level of 37 ng/mL, a higher galectin group increased the risk of all-cause mortality by 1.71-fold (95% CI 1.02–2.92, p = 0.048), even after adjustment for multiple CV risk factors. Mediation analysis showed that both the direct effect of the galectin-3 on mortality (β = 0.0368, bootstrapped 95% CI [0.0113–0.0622]) and the indirect effects were significant. AoAC score and high-sensitivity CRP levels significantly mediated the association between galectin-3 and mortality (total indirect effects: β = 0.0188, bootstrapped 95% CI [0.0066–0.0352]). This study suggests that the association between high galectin-3 and mortality may be partially mediated by higher VC and inflammatory state in HD patients.

OBJECTIVES This study examined the association between lack of compensation for COVID-19-related overtime work (LCCOW) and burnout among emergency medical service (EMS) providers in Seoul, Korea. METHODS We conducted a cross-sectional survey of 693 EMS providers in Seoul, Korea. Participants were classified into 3 groups according to their experience of coronavirus disease 2019 (COVID-19)-related overtime work and LCCOW: (1) “did not experience,” (2) “experienced and was compensated,” and (3) “experienced and was not compensated.” Burnout was measured using the Korean version of the Copenhagen Burnout Inventory, which has 3 subdomains: personal burnout (PB), work-related burnout (WRB), and citizen-related burnout (CRB). Multiple linear regression was applied to examine whether LCCOW was associated with burnout after adjusting for potential confounders. RESULTS In total, 74.2% of participants experienced COVID-19-related overtime work, and 14.6% of those who worked overtime experienced LCCOW. COVID-19-related overtime work showed a statistically non-significant association with burnout. However, the association differed by LCCOW. Compared to the “did not experience” group, the “experienced and was not compensated” group was associated with PB (β=10.519; 95% confidence interval [CI], 3.455 to 17.584), WRB (β=10.339; 95% CI, 3.398 to 17.280), and CRB (β=12.290; 95% CI, 6.900 to 17.680), whereas no association was observed for the “experienced and was compensated” group. Furthermore, an analysis restricted to EMS providers who worked overtime due to COVID-19 showed that LCCOW was associated with PB (β=7.970; 95% CI, 1.064 to 14.876), WRB (β=7.276; 95% CI, 0.270 to 14.283), and CRB (β=10.000; 95% CI, 3.435 to 16.565). CONCLUSIONS This study suggests that LCCOW could be critical in worsening burnout among EMS providers who worked overtime due to COVID-19.

This study investigated the effect of temperature on the aspect-ratio etching of SiO2 in CF4/H2/Ar plasma using patterned samples of a 200 nm trench in a low-temperature reactive-ion etching system. Lower temperatures resulted in higher etch rates and aspect ratios for SiO2. However, the plasma property was constant with the chuck temperature, indicated by the line intensity ratio from optical emission spectroscopy monitoring of the plasma. The variables obtained from the characterization of the etched profile for the 200 nm trench after etching were analyzed as a function of temperature. A reduction in the necking ratio affected the etch rate and aspect ratio of SiO2. The etching mechanism of the aspect ratio etching of SiO2 was discussed based on the results of the surface composition at necking via energy-dispersive X-ray spectroscopy with temperature. The results suggested that the neutral species reaching the etch front of SiO2 had a low sticking coefficient. The bowing ratio decreased with lowering temperature, indicating the presence of directional ions during etching. Therefore, a lower temperature for the aspect ratio etching of SiO2 could achieve a faster etch rate and a higher aspect ratio of SiO2 via the reduction of necking than higher temperatures.

This study delves into the application of convolutional neural networks (CNNs) in evaluating spinal sagittal alignment, introducing the innovative concept of incidence angles of inflection points (IAIPs) as intuitive parameters to capture the interplay between pelvic and spinal alignment. Pioneering the fusion of IAIPs with machine learning for sagittal alignment analysis, this research scrutinized whole-spine lateral radiographs from hundreds of patients who visited a single institution, utilizing high-quality images for parameter assessments. Noteworthy findings revealed robust success rates for certain parameters, including pelvic and C2 incidence angles, but comparatively lower rates for sacral slope and L1 incidence. The proposed CNN-based machine learning method demonstrated remarkable efficiency, achieving an impressive 80 percent detection rate for various spinal angles, such as lumbar lordosis and thoracic kyphosis, with a precise error threshold of 3.5°. Further bolstering the study’s credibility, measurements derived from the novel formula closely aligned with those directly extracted from the CNN model. In conclusion, this research underscores the utility of the CNN-based deep learning algorithm in delivering precise measurements of spinal sagittal parameters, and highlights the potential for integrating machine learning with the IAIP concept for comprehensive data accumulation in the domain of sagittal spinal alignment analysis, thus advancing our understanding of spinal health.

Introduction: Sasang Constitutional Medicine (SCM) is a type of traditional Korean medicine where patients are classified as one of four Sasang constitution types (Sasang type) and medications consisting of medicinal herbs are prescribed according to the Sasang type. Despite the importance of personalized medicine, the operation mechanism is largely unknown. To gain a better understanding, we investigated the compound information that composes Sasang type-specific personalized herbal medicines on both multivariate and univariate levels. Methods: Five machine learning classifiers including extremely randomized trees (ERT) were trained to investigate whether the Sasang type can be explained by compound information at the multivariate level. Hierarchical clustering was conducted to determine whether compounds are processed distributedly or specifically. Taxonomic and biosynthetic analyses were conducted on these compounds. A univariate level statistical test was conducted to provide more robust Sasang type-specific compound information. Results: Using the trained ERT classifier, sixty important compounds were extracted. The sixty compounds were clustered into three groups, corresponding to each Sasang type-prominent compounds, suggesting that most compounds have specific preference for the Sasang type. Structural and biosynthetic characteristics of these Sasang type-prominent compounds were determined based on taxonomy and pathway analyses. Fourteen compounds showed statistically significant relevance with the Sasang type. Additionally, we predicted the Sasang type of unknown herbs, which were confirmed by their biological effects in functional assays. Conclusion: This study investigated the personalized herbal medicines of the SCM using compound information. This study provided information on the chemical characteristics of the compounds that are essential for classifying the Sasang type of medicinal herbs, as well as predictions regarding the Sasang type of the commonly used but unidentified medicinal herbs.

Recently, Ni-rich ternary transition metal oxides (Li(NiCoMn)O2, NCM) containing more than 80% Ni have been extensively studied as high-capacity cathodes. Herein, we synthesized a quaternary cathode, Ni-rich Al-doped NCM, using a facile solvothermal method without an additional Al doping process. Compared to an undoped NCM cathode, the quaternary Ni-rich cathode exhibited improved lithium-ion battery performance (retentions of 82.0% and 65.8% after 100 and 250 cycles, respectively). In particular, the superior stability of the quaternary Ni-rich cathode may result from the stable cathode structure resulting from Al doping.

Abstract Background We sought to examine the association between childhood experience of parental death (CEPD) and adulthood suicidal ideation, and the mediating role of adulthood SES in the association. Methods We analyzed a nationally representative dataset of 8609 adults from the Korea Welfare Panel Study, which is a longitudinal cohort dataset in South Korea. CEPD was measured using a question: “During your childhood (0-17 years old), have you experienced the death of parents?” We classified responses of CEPD during 2006–2011 into ‘yes,’ and the others into ‘no.’ Suicidal ideation over the past year was assessed annually during 2012–2019. As a potential mediator, adulthood educational attainment and household income in 2011 were included in the analysis. Logistic regression was applied to examine the association of CEPD with adulthood suicidal ideation across age groups (early adulthood, 19–39 years old; middle adulthood, 40–59 years old; late adulthood, ≥60 years old), after excluding people who reported lifetime suicidal ideation in 2011. Causal mediation analysis using a parametric regression model was applied to examine the mediating role of adulthood SES in the association between CEPD and adulthood suicidal ideation. Results After adjusting for potential confounders including childhood SES, CEPD was significantly associated with adulthood suicidal ideation among the late adulthood group (OR: 1.43; 95% CI: 1.13–1.81), while the association was not statistically significant among the early; and middle adulthood groups. In mediation analysis of adulthood household income, both indirect association (ORNIE: 1.05; 95% CI: 1.02–1.09) and direct association (ORNDE: 1.37; 95% CI: 1.09–1.73) were statistically significant among the late adulthood group. In the mediation analysis of adulthood education attainment among the late adulthood, only a direct association was statistically significant (ORNDE: 1.43; 95% CI: 1.14–1.80). Conclusions These results suggest that CEPD could be a risk factor for adulthood suicidal ideation. Furthermore, the findings imply that income security policy might be necessary to reduce suicide among the late adulthood group.

This paper proposes a method to classify food types and to estimate meal intake amounts in pre- and post-meal images through a deep learning object detection network. The food types and the food regions are detected through Mask R-CNN. In order to make both pre- and post-meal images to a same capturing environment, the post-meal image is corrected through a homography transformation based on the meal plate regions in both images. The 3D shape of the food is determined as one of a spherical cap, a cone, and a cuboid depending on the food type. The meal intake amount is estimated as food volume differences between the pre-meal and post-meal images. As results of the simulation, the food classification accuracy and the food region detection accuracy are up to 97.57% and 93.6%, respectively.

Abstract Background Three-dimensional (3D) printing is widely used in the fabrication of dental prostheses; however, the influence of dental materials used for 3D printing on temporary restoration of fibroblasts in tissues is unclear. Thus, the influence of different dental materials on fibroblasts were investigated. Methods Digital light processing (DLP) type 3D printing was used. Specimens in the control group were fabricated by mixing liquid and powder self-curing resin restoration materials. The temporary resin materials used were Model, Castable, Clear-SG, Tray, and Temporary, and the self-curing resin materials used were Lang dental, Alike, Milky blue, TOKVSO CUREFAST, and UniFast III. Fibroblast cells were cultured on each specimen and subsequently post-treated for analysis. Morphology of the adhered cells were observed using a confocal laser scanning microscope (CLSM) and a scanning electron microscope (SEM). Results CLSM and SEM cell imaging revealed that the 3D printed material group presented better cell adhesion with well-distributed filopodia compared to that in the conventional resin material group. Cell proliferation was significantly higher in the 3D printing materials. Conclusion Superior cytocompatibility of the specimens fabricated through 3D printing and polishing process was demonstrated with the proof of better cell adhesion and higher cell proliferation.

Upon reentering the Earth’s atmosphere from space, a reentry vehicle becomes enshrouded in an ionization layer. This layer is known as the reentry plasma sheath and is caused by aerodynamic heating. Owing to the oscillation of charged particles in the reentry plasma sheath, the electromagnetic waves for communication between the vehicle and ground are attenuated. Analysis of the plasma density and attenuation of electromagnetic waves in a reentry plasma environment would require experimentation in an environment in which an actual aircraft reenters the atmosphere. Alternatively, an experiment in a large-scale plasma wind tunnel would be necessary. Unfortunately, these experiments would be extremely costly. Therefore, in this study, the reentry plasma was reproduced at laboratory scale using the hot refractory anode vacuum arc (HRAVA) method. In addition, the pressure in the vacuum chamber was used as a variable to probe the characteristics of the reentry plasma according to the altitude. The plasma density and attenuation of electromagnetic waves propagating through the plasma medium were measured using heterodyne interferometry and reflectometry capable of frequency analysis in the range of 10−35 GHz. The results confirmed that the plasma density and attenuation of the electromagnetic waves increased as the pressure in the vacuum chamber increased.

Abstract Background Despite the wide use of dental materials for CAD/CAM system in prosthetic treatment, the effect of the materials, which are used as dental implants core fabricated, on cells involved in dental implant osseointegration is uncertain. This study aimed to investigate and compare the effect of single core materials used for dental implants fabricated by the dental prostheses fabrication process and the CAD/CAM milling method on MC3T3-E1 cells. Methods The materials used for prostheses restoration in this experiment were Porcelain Fused Gold (P.F.G), Lithium disilicate glass ceramic (LiSi2), Zirconia (ZrO2), Nickel-Chromium (Ni-Cr) and Cobalt-Chromium (Co-Cr). MC3T3-E1 cells were cultured and used, the cell adhesion and morphology were observed and analyzed using confocal laser scanning microscopy (CLSM). Methoxyphenyl tetrazolium salt (MTS) and alkaline phosphatase (ALP) assay were used to observe the cell proliferation and differentiation. Results CLSM revealed irregular cell adhesion and morphology and the filopodia did not spread in the Ni-Cr specimen group. Significantly high cell proliferation was observed in the ZrO2 specimen group. The LiSi2 specimen group presented significantly high cell differentiation. Intergroup comparison of cell proliferation and differentiation between the Ni-Cr specimen group and all other specimen groups showed significant differences (p

OBJECTIVES This study was conducted to examine gender differences in under-reporting hiring discrimination by building a prediction model for workers who responded “not applicable (NA)” to a question about hiring discrimination despite being eligible to answer. METHODS Using data from 3,576 wage workers in the seventh wave (2004) of the Korea Labor and Income Panel Study, we trained and tested 9 machine learning algorithms using “yes” or “no” responses regarding the lifetime experience of hiring discrimination. We then applied the best-performing model to estimate the prevalence of experiencing hiring discrimination among those who answered “NA.” Under-reporting of hiring discrimination was calculated by comparing the prevalence of hiring discrimination between the “yes” or “no” group and the “NA” group. RESULTS Based on the predictions from the random forest model, we found that 58.8% of the “NA” group were predicted to have experienced hiring discrimination, while 19.7% of the “yes” or “no” group reported hiring discrimination. Among the “NA” group, the predicted prevalence of hiring discrimination for men and women was 45.3% and 84.8%, respectively. CONCLUSIONS This study introduces a methodological strategy for epidemiologic studies to address the under-reporting of discrimination by applying machine learning algorithms.

Background: Despite the importance of accurate Sasang type diagnosis, a unique form of Korean medicine, there have been concerns about consistency among diagnoses. We investigate a data-driven integrative diagnostic model by applying machine learning to a multicenter clinical dataset with comprehensive features. Methods: Extremely randomized trees (ERT), support vector machines, multinomial logistic regression, and K-nearest neighbor were applied, and performances were evaluated by cross-validation. The feature importance of the classifier was analyzed to understand which information is crucial in diagnosis. Results: The ERT classifier showed the highest performance, with an overall f1 score of 0.60 ± 0.060. The feature classes of body measurement, personality, general information, and cold–heat were more decisive than others in classifying Sasang types. Costal angle was the most informative feature. In pairwise classification, we found Sasang type-dependent distinctions that body measurement features played a key role in TE-SE and TE-SY datasets, while personality and cold–heat features showed importance in SE-SY dataset. Conclusion: Current study investigated a comprehensive diagnostic model for Sasang type using machine learning and achieved better performance than previous studies. This study helps data-driven decision making in clinics by revealing key features contributing to the Sasang type diagnosis.

Herbal formulae (HFs) are representative interventions in Korean medicine (KM) for the prevention and treatment of various diseases. Here, we proposed a machine learning-based approach for HF recommendation with enhanced interpretability and applicability. A dataset consisting of clinical symptoms, Sasang constitution (SC) types, and prescribed HFs was derived from a multicenter study. Case studies published over 10 years were collected and curated by experts. Various classifiers, oversampling methods, and data imputation techniques were comprehensively considered. The local interpretable model-agnostic explanation (LIME) technique was applied to identify the clinical symptoms that led to the recommendation of specific HFs. We found that the cascaded deep forest (CDF) model with data imputation and oversampling yielded the best performance on the training set and holdout test set. Our model also achieved top-1 and top-3 accuracies of 0.35 and 0.89, respectively, on case study datasets in which clinical symptoms were only partially recorded. We performed an expert evaluation on the reliability of interpretation results using case studies and achieved a score close to normal. Taken together, our model will contribute to the modernization of KM and the identification of an HF selection process through the development of a practically useful HF recommendation model.

A novel approach has been proposed for improving the performance of lithium-sulfur batteries (LSBs) with a carbon-based material as an interlayer between the cathode and separator. With this method, the cross-over of lithium polysulfides (LiPS) to the anode is suppressed, increasing reutilization of the sulfur cathode. In this study, activated carbons (ACs) were prepared using coffee waste as a carbon source and potassium hydroxide (KOH) as an activation agent at various reaction temperatures ranging from 500 to 800 °C. With the rise in heating temperature, the specific surface areas, micro-surface areas, and micro-pore volumes of the AC samples gradually increased. In particular, the AC sample prepared at 800 °C and used as a functional separator for LSB exhibited improved capacity and cycling performance while suppressing the LiPS shuttle effect.

Temporal action localization from untrimmed videos is a fundamental task for real-world computer vision applications such as video surveillance systems. Even though a great deal of research attention has been paid to the problem, precise localization of human activities at a frame level still remains as a challenge. In this paper, we propose CoarseFine networks that learn highly discriminative features without loss of time granularity with two streams: the coarse and fine networks. The coarse network aims to classify the action category based on the global context of a video by taking advantage of the description power of successful action recognition models. On the other hand, the fine network does not deploy temporal pooling constrained with a low channel capacity. The fine network is specialized to identify the per-frame location of actions based on local semantics. This approach enables CoarseFine networks to learn find-grained representations without any temporal information loss. Our extensive experiments on two challenging benchmarks, THUMOS14 and ActivityNet-v1.3, validate that our proposed method provides a higher accuracy compared to the state-of-the-art by a remarkable margin in per-frame labeling and temporal action localization tasks while the computational cost is significantly reduced.

This paper proposes receive diversity with power adaptation for a non-orthogonal multiple access (NOMA) system in a multi-user circumstance. Since the basic concept of power domain NOMA divides user data by the difference of power, successive interference cancellation (SIC) is necessary to extract user data. In the process of SIC calculation, the error propagation has occurred. Therefore, signal-to-interference-plus-noise ratio (SINR) decreases as the number of user entity increases in a power domain NOMA system. In order to mitigate the inter-user interference, data are allocated to subcarriers with an instantaneous user grouping scheme. With the subcarrier structure of the instantaneous user grouping scheme, this paper proposes receive diversity to improve the bit error rate (BER). However, since the diversity method decreases the sum capacity of the system, a power adaptation method is also proposed to compensate the sum capacity. The simulation results show that the proposed method improves the BER performance than the conventional scheme and increases sum capacity that is decreased by the receiver diversity scheme.

OBJECTIVES This study aimed to examine health disparities between prisoners and the general population in Korea. METHODS We sought to estimate the prevalence of 17 physical and mental diseases using the nationwide medication prescription dataset among the total population of prisoners (n=57,541) in Korea. Age- and sex- standardized prevalence ratios (SPRs) were estimated to compare the disease prevalence between the prisoners and the general population. The disease prevalence for the general population was calculated from the prescription dataset for a representative of the Korean population (n=926,246) from the 2013 Korean National Health Insurance Service-National Sample Cohort. Furthermore, the prevalence of these diseases was compared between prisoners and a low-income segment of the general population (n=159,781). RESULTS Compared to the general population, prisoners had higher prevalence of almost all physical and mental diseases, including hyperlipidemia (SPR, 20.18; 95% confidence interval [CI], 19.43 to 20.94), pulmonary tuberculosis (SPR, 9.58; 95% CI, 7.91 to 11.50), diabetes (SPR, 6.13; 95% CI, 5.96 to 6.31), cancer (SPR, 2.36; 95% CI, 2.07 to 2.68), and depression (SPR, 46.73; 95% CI, 44.14 to 49.43). When compared with the low-income population segment, higher prevalence were still found among prisoners for most diseases, including pulmonary tuberculosis (SPR, 6.39; 95% CI, 5.27 to 7.67) and depression (SPR, 34.71; 95% CI, 32.79 to 36.72). CONCLUSIONS We found that prisoners were more likely to be unhealthy than the general population, even in comparison with a low-income segment of the general population in Korea.

Background: The purpose of this in vitro study is to fabricate a novel metal–ceramic prosthesis with a porous structure, to compensate for the disadvantages associated with the design of existing prostheses, and to measure the internal fit of this prosthesis. Materials and Methods: In this in vitro study, the mandibular first molar was scanned from the dental computer-aided-design to design a 3 mm porous structure frame. The frame was produced using the lamination method and fired in a pressed ceramic. For comparison, pore-free specimens were fabricated by selective laser sintering (SLS) as described above, and porous specimens were fabricated by casting (total n = 30). The internal fit was then measured using a digital microscope (at 100× magnification), and the data were analyzed using one-way ANOVA (α = 0.05). Results: The total mean internal discrepancies for each group were 42.32 ± 22.50 μm for the porous structure SLS group (PS-group), 107.54 ± 38.75 μm for no-porous casting group (group), and 121.36 ± 50.19 μm for the no-porous SLS group (group), with significant differences (P < 0.05) among all groups. Conclusion: The internal discrepancies of porous structure crown fabricated by SLS were smaller than that of no-porous crown fabricated by casting and SLS. Based on these laboratory findings, further studies should be conducted to evaluate the feasibility of the newly designed porous structure and press ceramic prosthesis to determine whether they can be applied in clinical practice.

Background/purpose: To evaluate the reproducibility of the marginal and internal gaps of metal copings fabricated using dental micro-stereolithography (μ-SLA), which is an additive manufacturing system. Materials and methods: A study cast of abutment tooth 46 was made from type-IV dental stone and was scanned to create a standard triangulation language file. Arrays of one (ORM), three (TRM), and six (SRM) resin copings were then fabricated on the μ-SLA build platform using investment, burnout, and casting (n = 12). The marginal and internal gaps of these metal copings were measured using a silicone-replica technique with a digital microscope (×160). The data obtained were analyzed using a non-parametric Kruskal–Wallis H test, a post-hoc Mann–Whitney U test, and a Bonferroni correction. Results: The mean and standard deviation of the marginal gap for each group were measured and found to be 81.1 and 53.2 μm, 68.3 and 44.8 μm, and 90.3 and 57.7 μm for ORM, TRM, and SRM, respectively. There were no statistical differences in the marginal gaps of the three groups (p > 0.05). Conclusion: The marginal and internal gap of ORM, TRM and SRM groups were considered clinically acceptable. Keywords: CAD/CAM, marginal gap, micro-stereolithography, Ni–Cr

A smart-skin antenna structure is investigated for active flutter control with piezoelectric sensors and actuators. The skin antenna is designed as a multilayer sandwich structure with a dielectric polymer to perform the role of antenna or radar structures. The governing equations are developed according to the first-order shear deformation theory, and von Karman strain–displacement relationships are used for the moderate geometrical nonlinearity. To consider the supersonic airflow, first-order piston theory is performed for the aerodynamic pressures. The linear quadratic regulator (LQR) method is applied as a control algorithm, and Newmark’s method is studied to obtain the numerical results. In the present study, the effects of placements and shape of piezoelectric patches are discussed on the flutter control of the model in detail. In addition, the numerical results show that the skin antenna model can effectively suppress the panel flutter behaviors of the model, optimal conditions of piezoelectric patches are obtained for skin antenna structures.

Speech recognition consists of converting input sound into a sequence of phonemes, then finding text for the input using language models. Therefore, phoneme classification performance is a critical factor for the successful implementation of a speech recognition system. However, correctly distinguishing phonemes with similar characteristics is still a challenging problem even for state-of-the-art classification methods, and the classification errors are hard to be recovered in the subsequent language processing steps. This paper proposes a hierarchical phoneme clustering method to exploit more suitable recognition models to different phonemes. The phonemes of the TIMIT database are carefully analyzed using a confusion matrix from a baseline speech recognition model. Using automatic phoneme clustering results, a set of phoneme classification models optimized for the generated phoneme groups is constructed and integrated into a hierarchical phoneme classification method. According to the results of a number of phoneme classification experiments, the proposed hierarchical phoneme group models improved performance over the baseline by 3%, 2.1%, 6.0%, and 2.2% for fricative, affricate, stop, and nasal sounds, respectively. The average accuracy was 69.5% and 71.7% for the baseline and proposed hierarchical models, showing a 2.2% overall improvement.

In recent years, efforts to reduce greenhouse gas emissions have continued worldwide. In the construction industry, a large amount of CO2 is generated during the production of Portland cement, and various studies are being conducted to reduce the amount of cement and enable the use of cement substitutes. Ferronickel slag is a by-product generated by melting materials such as nickel ore and bituminous coal, which are used as raw materials to produce ferronickel at high temperatures. In this study, we investigated the fluidity, microhydration heat, compressive strength, drying shrinkage, and carbonation characteristics of a ternary cement mortar including ferronickel-slag powder and fly ash. According to the test results, the microhydration heat of the FA20FN00 sample was slightly higher than that of the FA00FN20 sample. The 28-day compressive strength of the FA20FN00 mix was approximately 39.6 MPa, which was higher than that of the other samples, whereas the compressive strength of the FA05FN15 mix including 15% of ferronickel-slag powder was approximately 11.6% lower than that of the FA20FN00 mix. The drying shrinkage of the FA20FN00 sample without ferronickel-slag powder was the highest after 56 days, whereas the FA00FN20 sample without fly ash showed the lowest shrinkage compared to the other mixes.

Objectives: In South Korea (hereafter Korea), the number of adolescent offspring of immigrants has rapidly increased since the early 1990s, mainly due to international marriage. This research sought to examine the association between the experience of school violence and mental health outcomes, and the role of help-seeking behaviors in the association, among biethnic adolescents in Korea. Methods: We analyzed cross-sectional data of 3627 biethnic adolescents in Korea from the 2012 National Survey of Multicultural Families. Based on the victim’s help-seeking behavior, adolescents who experienced school violence were classified into three groups: ‘seeking help’ group; ‘feeling nothing’ group; ‘not seeking help’ group. Multivariate logistic regression was applied to examine the associations between the experience of school violence and depressive symptoms for males and females separately. Results: In the gender-stratified analysis, school violence was associated with depressive symptoms in the ‘not seeking help’ (odds ratio [OR], 7.05; 95% confidence interval [CI], 3.76 to 13.23) and the ‘seeking help’ group (OR, 2.77; 95% CI, 1.73 to 4.44) among male adolescents after adjusting for potential confounders, including the nationality of the immigrant parent and Korean language fluency. Similar associations were observed in the female groups. However, in the ‘feeling nothing’ group, the association was only significant for males (OR, 8.34; 95% CI, 2.82 to 24.69), but not females (OR, 0.77; 95% CI, 0.18 to 3.28). Conclusions: This study suggests that experience of school violence is associated with depressive symptoms and that the role of victims’ help-seeking behaviors in the association may differ by gender among biethnic adolescents in Korea.

The performance of a long short-term memory (LSTM) recurrent neural network (RNN)-based language model has been improved on language model benchmarks. Although a recurrent layer has been widely used, previous studies showed that an LSTM RNN-based language model (LM) cannot overcome the limitation of the context length. To train LMs on longer sequences, attention mechanism-based models have recently been used. In this paper, we propose a LM using a neural Turing machine (NTM) architecture based on localized content-based addressing (LCA). The NTM architecture is one of the attention-based model. However, the NTM encounters a problem with content-based addressing because all memory addresses need to be accessed for calculating cosine similarities. To address this problem, we propose an LCA method. The LCA method searches for the maximum of all cosine similarities generated from all memory addresses. Next, a specific memory area including the selected memory address is normalized with the softmax function. The LCA method is applied to pre-trained NTM-based LM during the test stage. The proposed architecture is evaluated on Penn Treebank and enwik8 LM tasks. The experimental results indicate that the proposed approach outperforms the previous NTM architecture.

Dental hard tissue formation and bone turnover are required for tooth eruption. 4-Hexylresorcinol (4HR) accelerates tooth movement by increasing bone turnover in orthodontic treatment. This study aimed to evaluate the following: (1) the effect of 4HR application on the expression of proteins associated with tooth formation, and (2) the effect of 4HR application on mandibular incisor eruption rate in a rat model. Primary cultured pulp cells received either 4HR (1 to 100 µM) or solvent only; western blotting was performed for transforming growth factor-beta 1 (TGF-β1), bone morphogenic protein-2/4 (BMP-2/4), runt-related transcription factor 2 (Runx2), osterix (OSX), dentin sialophosphoprotein (DSPP), and parathyroid hormone-related protein receptor (PTHrP-R). In in vivo study, rats (15 males and 15 females) received either solvent or 0.128 mg/kg or 12.8 mg/kg of 4HR via subcutaneous injection; mandibular incisor eruption rate was subsequently recorded. Immunohistochemical staining and western blotting for TGF-β1, BMP-2/4, Runx2, OSX, DSPP, and PTHrP-R were performed in the mandibular tissue samples. 4HR administration was found to increase TGF-β1, BMP-2/4, Runx2, OSX, DSPP, and PTHrP-R expression in both cell culture and tissue samples. Immunohistochemical staining of some markers showed site-specific expression, thereby indicating programmed differentiation of odontoblasts and ameloblasts. The eruption rate was significantly higher in the 12.8 mg/kg 4HR-administered group than in the untreated control (p = 0.001 and 0.010 for males and females, respectively). Collectively, 4HR administration increased the expression of markers related to dental hard tissue formation and accelerated the eruption rate of incisors in rats.

This study prepared a basic framework for the development and improvement of Korean Dental Technicians' Licensing Examination, based on actual test questions. A peer review was conducted to ensure relevance to current practices in dental technology. For the statistical analysis, 1000 dental laboratory technicians were selected; specialists in dental laboratory technology (laboratory owners, educators, etc.) were involved in creating valid and reliable questions. Results indicated that examination subjects should be divided into three categories: basic dental laboratory theory, dental laboratory specialties, and a practical examination. To ensure relevance to current practice, there should be less emphasis on basic dental laboratory theory, including health-related laws, and more emphasis on dental laboratory specialties. Introduction to dental anatomy should be separated from oral anatomy and tooth morphology; and fixed prosthodontics should be separated from crown and bridge technology and dental ceramics technology. Removable orthodontic appliance technology should be renamed 'orthodontic laboratory technology'. There should be less questions related to health related law, oral anatomy, dental hygiene, dental materials science and inlay, while the distribution ratio of questions related to tooth morphology should be maintained. There should be a decrease in the distribution ratio of questions related to crown and bridge technology, dental ceramics technology, complete dentures and removable partial dentures technology, and orthodontic laboratory technology. In the practical examination, the current multiple choice test should be replaced with tooth carving using wax or plaster. In dental laboratory specialties, subjects related to contemporary dental laboratory technology should be included in the test items.

In this paper, we present a method that integrates a Grammar Transducer as an external language model to enhance the accuracy of the pre-trained Korean End-to-end (E2E) Automatic Speech Recognition (ASR) model. The E2E ASR model utilizes the Connectionist Temporal Classification (CTC) loss function to derive hypothesis sentences from input audio. However, this method reveals a limitation inherent in the CTC approach, as it fails to capture language information from transcript data directly. To overcome this limitation, we propose a fusion approach that combines a clause-level n-gram language model, transformed into a Weighted Finite-State Transducer (WFST), with the E2E ASR model. This approach enhances the model's accuracy and allows for domain adaptation using just additional text data, avoiding the need for further intensive training of the extensive pre-trained ASR model. This is particularly advantageous for Korean, characterized as a low-resource language, which confronts a significant challenge due to limited resources of speech data and available ASR models. Initially, we validate the efficacy of training the n-gram model at the clause-level by contrasting its inference accuracy with that of the E2E ASR model when merged with language models trained on smaller lexical units. We then demonstrate that our approach achieves enhanced domain adaptation accuracy compared to Shallow Fusion, a previously devised method for merging an external language model with an E2E ASR model without necessitating additional training. [ABSTRACT FROM AUTHOR]