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Abstract American white (Quercus alba L.) oak casks have been used for liquid storage for centuries. Their use in aged spirits is critical to imparting flavor and mouthfeel to the final product. The reason that barrels retain liquid has been hypothesized to be the result of abundant physiological structures called tyloses in parenchyma tissues and medullary rays in white oak. Using non-destructive X-ray computed tomography (XRCT) imaging, we reveal an unprecedented view of tylose structure and quantify the pore-filling capacity of tyloses in white oak that underscores the liquid retention we observe in casks. We show that pores of white oaks are filled with sevenfold higher tylose volume compared to northern red oak (Q. rubra), consistent with prior literature that casks made from white oak retain liquid while red oak fails to do so. We propose that XRCT represents a methodological standard for observing these complex structures and should be employed to understand the many questions related to liquid losses from casks, cultural treatment of casks, and the influence of climate change on oak tyloses in the future.

Purpose Sepsis-related deaths occur during both the early proinflammatory and the late immunosuppressive phases of the condition. The balance of pro- and anti-inflammatory responses is influenced by damaged cells that die via either proinflammatory necroptosis or anti-inflammatory apoptosis. Both forms of cell death may be mediated by reactive oxygen species (ROS) generated during the proinflammatory response. Recent evidence suggests that exercise training boosts antioxidative capacity and could offer protection against sepsis. Given these findings, we aimed to examine the impact of exercise training on neural cell death in the context of sepsis. Methods We assessed the effectiveness of exercise in reducing ROS production and the inflammatory response using a cecal ligation and puncture (CLP)-induced sepsis model. Forty C57BL/6N male mice were randomly divided into 2 groups: control (CLP-Con; n=20) and experimental (CLP-Ex; n=20). Before the induction of sepsis by CLP, the CLP-Ex mice underwent interval training on a treadmill 3 days per week for 8 weeks. Each day involved 10 cycles of 2 minutes at 8 m/min and 2 minutes at 15 m/min. After the CLP procedure, we monitored the survival of 10 mice from each group over a 30-hour period. Results The findings indicated that exercise training increased the survival rate among mice with CLP-induced sepsis by enhancing antioxidative capacity and delaying the transition from a hyperdynamic to an immunosuppressive state. Conclusions Exercise training may delay the progression from the hyperdynamic state to the hypodynamic phase of sepsis by increasing antioxidant capacity and reducing apoptotic cell death.

Background: Recently, plant-derived exosome-like nanoparticles (PDENs) have been isolated, and active research was focusing on understanding their properties and functions. In this study, the characteristics and molecular properties of ginseng root-derived exosome-like nanoparticles (GrDENs) were examined in terms of skin protection. Methods: HPLC-MS protocols were used to analyze the ginsenoside contents in GrDENs. To investigate the beneficial effect of GrDENs on skin, HaCaT cells were pre-treated with GrDENs (0–2 × 109 particles/mL), and followed by UVB irradiation or H2O2 exposure. In addition, the antioxidant activity of GrDENs was measured using a fluorescence microscope or flow cytometry. Finally, molecular mechanisms were examined with immunoblotting analysis. Results: GrDENs contained detectable levels of ginsenosides (Re, Rg1, Rb1, Rf, Rg2 (S), Gyp17, Rd, C-Mc1, C–O, and F2). In UVB-irradiated HaCaT cells, GrDENs protected cells from death and reduced ROS production. GrDENs downregulated the mRNA expression of proapoptotic genes, including BAX, caspase-1, -3, -6, -7, and -8 and the ratio of cleaved caspase-8, -9, and -3 in a dose-dependent manner. In addition, GrDENs reduced the mRNA levels of aging-related genes (MMP2 and 3), proinflammatory genes (COX-2 and IL-6), and cellular senescence biomarker p21, possibly by suppressing activator protein-1 signaling. Conclusions: This study demonstrates the protective effects of GrDENs against skin damage caused by UV and oxidative stress, providing new insights into beneficial uses of ginseng. In particular, our results suggest GrDENs as a potential active ingredient in cosmeceuticals to promote skin health.

The Korean language has written (formal) and spoken (phonetic) forms that differ in their application, which can lead to confusion, especially when dealing with numbers and embedded Western words and phrases. This fact makes it difficult to automate Korean speech recognition models due to the need for a complete transcription training dataset. Because such datasets are frequently constructed using broadcast audio and their accompanying transcriptions, they do not follow a discrete rule-based matching pattern. Furthermore, these mismatches are exacerbated over time due to changing tacit policies. To mitigate this problem, we introduce a data-driven Korean spoken-to-written transcription conversion technique that enhances the automatic conversion of numbers and Western phrases to improve automatic translation model performance.

Abstract Hydrophobic surfaces have a wide range of applications, such as water harvesting, self-cleaning, and anti-biofouling. However, traditional methods of achieving hydrophobicity often involve the use of toxic materials such as fluoropolymers. This study aims to create controllable wettability surfaces with a three-dimensional geometry using a laser base powder bed fusion (PBF) process with commercially pure titanium (CP-Ti) and silicone oil as non-toxic materials. The optimal PBF process parameters for fabricating micropillar structures, which are critical for obtaining the surface roughness necessary for achieving hydrophobic properties, were investigated experimentally. After fabricating the micropillar structures using PBF, their surface energy was reduced by treatment with silicone oil. Silicone oil provides a low-surface-energy coating that contributes to the water-repellent nature of hydrophobic surfaces. The wettability of the treated CP-Ti surfaces was evaluated based on the diameter of the pillars and the space between them. The structure with the optimal diameter and spacing of micropillars exhibited a high contact angle (156.15°). A pronounced petal effect (sliding angle of 25.9°) was achieved because of the morphology of the pillars, indicating the controllability of wetting. The micropillar diameter, spacing, and silicone oil played crucial roles in determining the water contact and sliding angle, which are key metrics for surface wettability.

Abstract Background Sarcopenic obesity, defined as the coexistence of low muscle mass and high adiposity, is associated with cardiovascular disease (CVD) and mortality. However, to what extent sarcopenia contributes to these risks independently or in conjunction with other cardiovascular risk factors remains unclear. This study aimed to investigate the association of low muscle mass, central obesity (COB), metabolic abnormalities, and their combinations with CVD and mortality risk. Methods This cross‐sectional analysis used data from the National Health and Nutrition Examination Survey 1999–2006 and 2011–2018. Participants aged >20 years and with reported whole‐body dual X‐ray absorptiometry data were included. Participants were divided into eight groups based on low muscle mass, metabolic abnormalities, and COB status. Results The mean age of participants was 55 years, and 50.4% of participants were male. Low muscle mass was observed in 2472 (14.6%) out of 16 839 participants. Among the eight groups, the metabolically unhealthy COB group with low muscle mass had the highest hazard ratio (HR) for all‐cause mortality (HR, 2.00; 95% CI, 1.56–2.56; P

Existing archive formats provide encryption to protect data, but vulnerabilities in these formats can lead to data leakage. This study proposes a novel attack to leak original data from encrypted archive files by exploiting the integrity check value and unencrypted metadata. The proposed attack obtains the size of the original data and the integrity check value by parsing the encrypted file, and identifies the original data by leveraging password-cracking techniques. Experiments with seven archive formats and seven utilities demonstrate the effectiveness of our proposed attack, successfully leaking data from 11 out of 20 encrypted files. This research uncovers vulnerabilities in existing archive formats and contributes to the design of more secure archiving systems.

An eye diagram, a critical metric in signal integrity analysis for high-speed interconnects such as packages, interposer, and printed circuit boards (PCBs), is generated by superposition of the received waveform. Obtaining an eye diagram is time-consuming, thus signal integrity analysis is inefficient. This article reviews that have been proposed to overcome this limitation. The statistical eye diagram provides a probability distribution depending on a sampling time and voltage, therefore it can be expanded to other metrics, such as the bit-error rate and shmoo plot. This article introduces previous research on statistical eye diagrams applied to complementary metal-oxide-semiconductors (CMOSs), noise, and high-speed systems. The methods applied to CMOSs include asymmetry between the P/NMOS transistors and the nonlinearity of the CMOS. The methods applied to noise include signal and power noise. The methods applied to high-speed systems include equalizers, signaling, encoding, linear feedback shift register, and error correction code.

This study investigated a novel forward body bias (FBB) analysis to optimize the threshold voltage ( $\text{V}_{\mathrm {th}}$ ) at cryogenic temperatures in the latest dynamic random-access memory (DRAM). Electrical measurements were conducted to analyze the cryogenic body bias effect in terms of performance, reliability, and short-channel effect in two types of transistors: DRAM peripheral low $\text{V}_{\mathrm {th}}$ transistors (Peri LVT) and buried-channel-array transistors (BCAT). At 77 K, the $\text{V}_{\mathrm {th}}$ shift ( $\Delta \text{V}_{\mathrm {th}}$ ) in BCAT was larger than that in Peri LVT due to the difference in channel doping concentration. It was observed that only BCAT experienced a decrease in saturation drain current ( $\text{I}_{\mathrm {d.sat}}$ ) at cryogenic temperature because of the large $\Delta \text{V}_{\mathrm {th}}$ . To compensate for the $\Delta \text{V}_{\mathrm {th}}$ , FBB was applied to transistors. As a result, FBB effectively controlled the $\text{V}_{\mathrm {th}}$ and improved carrier mobility. Furthermore, this study demonstrated that FBB reduced hot-carrier degradation (HCD) at cryogenic temperature and improved short-channel effect, such as drain-induced barrier lowering (DIBL). These findings offer valuable solutions for optimizing cryogenic memory operation in quantum computing applications.

Timer options, which were first introduced by Société Générale Corporate and Investment Banking in 2007, are financial securities whose payoffs and exercise are determined by a random time associated with the accumulated realized variance of the underlying asset, unlike vanilla options exercised at the prescribed maturity date. In this paper, taking account of the correlation between the underlying asset price and volatility, we investigate the pricing of timer options under the constant elasticity of variance (CEV) model, proposed by Cox and Ross [10], taking advantage of the approach of asymptotic analysis. Additionally, we validate the pricing precision of the approximate formula for timer options using the Monte Carlo method. We conduct numerical experiments based on our corrected prices and analyze price sensitivities concerning various model parameters, with a focus on the value of elasticity.

A simple model that can assess the river rehabilitation using rehabilitation potential index (RPI) of fish habitat has been developed. This model calculates the RPI using three indicators: the habitat suitability index (HSI), niche breadth (NB), and niche overlap (NO). These indicators were estimated by using environmental factors that affect fish habitat in the river and then RPI was obtained by combining the indicators. However, the model showed some limitations. For example, the representative NO was obtained by summing each NO, which does not reflect the difference of the number of fish species in rivers. This study improved the estimation method of NO by using the averaged NO that can consider different kinds of species in each river. In addition, the previous study used standardization method for making three indicator values in the range of 0 to 1. However, the standardized indicators can distort the relative size when we compare with the original data. In this study, we have selected alternative methods to calculate indicators, eliminating the need for standardization. Along with these improvements, this study developed a new RPI estimation model by reviewing various methods and selecting the proper ones. The RPIs of the two models were estimated using fish abundance and habitat environmental factors from 57 sites in Han river, Geum river, and Nakdong river in Korea. We compared the results of the existing model with the new model using root mean square error (RMSE) and mean absolute deviation (MAD). The new model showed the reduced uncertainty and enhanced applicability in comparison to the previous model.

Hydrogen (H2) is increasingly recognized as a key player in the journey towards carbon neutrality, with ammonia (NH3) and formic acid (FA) emerging as significant hydrogen vectors. This review highlights advancements in catalyst efficiency for FA synthesis from CO2, particularly with bismuth (Bi) and tin (Sn) catalysts. It investigates diverse NH3 and FA production methodologies, such as electrochemical, thermochemical, and photochemical processes, and underscores the integration of renewables to address their energy demands. The study also reviews novel materials like metal-organic frameworks (MOFs) and carbon-based catalysts that could enhance catalytic effectiveness. Transitioning from lab-scale models to industrial-scale applications requires addressing catalyst longevity and process enhancement, and it suggests investigating hybrid systems that might offer improved efficiency and yields. Concluding with a directive for future research, the study advocates for scalable, economically viable, and environmentally sustainable CO2 conversion technologies, underscoring the essential roles of NH3 and FA in a future with reduced carbon emissions.

ABSTRACTAlternative irrigation waters (rivers, ponds, and reclaimed water) can harbor bacterial foodborne pathogens like Salmonella enterica and Listeria monocytogenes, potentially contaminating fruit and vegetable commodities. Detecting foodborne pathogens using qPCR-based methods may accelerate testing methods and procedures compared to culture-based methods. This study compared detection of S. enterica and L. monocytogenes by qPCR (real-time PCR) and culture methods in irrigation waters to determine the influence of water type (river, pond, and reclaimed water), season (winter, spring, summer, and fall), or volume (0.1, 1, and 10 L) on sensitivity, accuracy, specificity, and positive (PPV), and negative (NPV) predictive values of these methods. Water samples were collected by filtration through modified Moore swabs (MMS) over a 2-year period at 11 sites in the Mid-Atlantic U.S. on a bi-weekly or monthly schedule. For qPCR, bacterial DNA from culture-enriched samples (n = 1,990) was analyzed by multiplex qPCR specific for S. enterica and L. monocytogenes. For culture detection, enriched samples were selectively enriched, isolated, and PCR confirmed. PPVs for qPCR detection of S. enterica and L. monocytogenes were 68% and 67%, respectively. The NPV were 87% (S. enterica) and 85% (L. monocytogenes). Higher levels of qPCR/culture agreement were observed in spring and summer compared to fall and winter for S. enterica; for L. monocytogenes, lower levels of agreement were observed in winter compared to spring, summer, and fall. Reclaimed and pond water supported higher levels of qPCR/culture agreement compared to river water for both S. enterica and L. monocytogenes, indicating that water type may influence the agreement of these results.IMPORTANCEDetecting foodborne pathogens in irrigation water can inform interventions and management strategies to reduce risk of contamination and illness associated with fresh and fresh-cut fruits and vegetables. The use of non-culture methods like qPCR has the potential to accelerate the testing process. Results indicated that pond and reclaimed water showed higher levels of agreement between culture and qPCR methods than river water, perhaps due to specific physiochemical characteristics of the water. These findings also show that season and sample volume affect the agreement of qPCR and culture results. Overall, qPCR methods could be more confidently utilized to determine the absence of Salmonella enterica and Listeria monocytogenes in irrigation water samples examined in this study.

Abstract Optical properties of single emitters can be significantly improved through the interaction with plasmonic structures, leading to enhanced sensing and imaging capabilities. In turn, single emitters can act as sensitive probes of the local electromagnetic field surrounding plasmonic structures, furnishing fundamental insights into their physics and guiding the design of novel plasmonic devices. However, the interaction of emitters in the proximity to a plasmonic nanostructure causes distortion, which hinders precise estimation of position and polarization state and is one of the reasons why detection and quantification of molecular processes yet remain fundamentally challenging in this era of super-resolution. Here, we investigate axially defocused images of a single fluorescent emitter near metallic nanostructure, which encode emitter positions and can be acquired in the far-field with high sensitivity, while analyzing the images with pattern matching algorithm to explore emitter-localized surface plasmon interaction and retrieve information regarding emitter positions. Significant distortion in defocused images of fluorescent beads and quantum dots near nanostructure was observed and analyzed by pattern matching and finite-difference time-domain methods, which revealed that the distortion arises from the emitter interaction with nanostructure. Pattern matching algorithm was also adopted to estimate the lateral positions of a dipole that models an emitter utilizing the distorted defocused images and achieved improvement by more than 3 times over conventional diffraction-limited localization methods. The improvement by defocused imaging is expected to provide a way of enhancing reliability when using plasmonic nanostructure and diversifying strategies for various imaging and sensing modalities.

Flextensional transducers have been widely used as low-frequency projectors, and these characteristics can be used to develop hydrophones with wider receiver bandwidth and higher sensitivity than conventional products in low-frequency ranges. In this work, we designed flextensional hydrophones of all classes, and compared their acoustic receiver performance to select the most suitable class for a low-frequency broadband hydrophone. For this purpose, basic models of the hydrophones were constructed for all classes and the effects of various structural parameters on the acoustic receiver characteristics of the hydrophones were analyzed. Based on the results, the structure of the flextensional hydrophone of each class was designed to have the maximum receiver bandwidth by an optimization technique while maintaining the receiver voltage sensitivity over a certain level. A comparison of the designed performance led to the selection of the class IV flextensional hydrophone as the most promising one with the widest receiver fractional bandwidth and highest sensitivity.

Androgenetic alopecia is a genetic disorder that commonly causes progressive hair loss in men, leading to diminished self-esteem. Although cannabinoids extracted from Cannabis sativa are used in hair loss treatments, no study has evaluated the effects of germinated hemp seed extract (GHSE) and exosomes derived from the calli of germinated hemp seeds on alopecia. Therefore, this study aimed to demonstrate their preventive effects against alopecia using various methodologies, including quantitative PCR, flow cytometry, ELISA, and immunocytochemistry. Our research highlights the preventive functions of GHSE (GE2000: 2000 µg/mL) and exosomes from the calli of germinated hemp seeds (E40: 40 μg/mL) in three biochemical categories: genetic modulation in hair follicle dermal papilla stem cells (HFDPSCs), cellular differentiation, and immune system modulation. Upon exposure to dihydrotestosterone (DT), both biomaterials upregulated genes preventing alopecia (Wnt, β-catenin, and TCF) in HFDPSCs and suppressed genes activating alopecia (STAT1, 5α-reductase type 1, IL-15R). Additionally, they suppressed alopecia-related genes (NKG2DL, IL2-Rβ, JAK1, STAT1) in CD8+ T cells. Notably, E40 exhibited more pronounced effects compared to GE2000. Consequently, both E40 and GE2000 effectively mitigated DT-induced stress, activating mechanisms promoting hair formation. Given the limited research on alopecia using these materials, their pharmaceutical development promises significant economic and health benefits.

Study Region: Naeseongcheon Stream Basin, Korea Study focus: We conducted a flood risk assessment using grid data for each indicator and a flood risk map. Using a grid-based flood risk map, grid data for the nine indicators included in the Hazard, Exposure, and Vulnerability items from 2016 to 2020 were selected and indexed by scoring each quantile interval. The entropy weight and Euclidean distance were then applied to calculate the grid-based item indices and flood risk index. Finally, the Expected Annual Damage (EAD) ranking calculated using the Korean flood risk assessment model was compared with the flood risk assessment ranking using grid data for 17 sub-basins in the Naeseongcheon Stream Basin for 2018. New hydrological insights for the region: The analysis showed that the absolute error was improved by the application of the flood damage rate and selection of the grid cells including damage targets. When comparing the flood risk assessment ranking of each of the 17 sub-basins with the EAD ranking, the mean absolute error was 1.882, and the root mean square error was 3.009. This confirms that qualitative flood risk assessment using grid data and flood risk maps can provide as much basin-level risk analysis as quantitative flood risk assessment. Therefore, the method presented in this study can be utilized as a useful decision-support tool for the government’s basin-level disaster prevention projects.

The CO2 trap mechanisms during carbon capture and storage (CCS) are classified into structural, residual, solution, and mineral traps. The latter is considered as the most permanent and stable storage mechanism as the injected CO2 is stored in solid form by the carbon mineralization. In this study, the carbon mineralization process in geological CO2 storage in basalt, sandstone, carbonate, and shale are reviewed. In addition, relevant studies related to the carbon mineralization mechanisms, and suggestions for future research directions are proposed. The carbon mineralization is defined as the conversion of CO2 into stable carbon minerals by reacting with divalent cations such as Ca2+, Mg2+, or Fe2+. The process is mainly affected by rock types, temperature, fluid composition, injected CO2 phase, competing reaction, and nucleation. Rock properties such as permeability, porosity, and rock strength can be altered by the carbon mineralization. Since changes of the properties are directly related to injectivity, storage capacity, and stability during the geological CO2 storage, the carbon mineralization mechanism should be considered for an optimal CCS design.

Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an obstacle to improving the accuracy and efficiency of hydrological analysis. To address this issue, we developed a machine learning algorithm-based precipitation data recovery tool to detect and predict missing precipitation data at observatories. This study investigated 30 weather stations in South Korea, evaluating the applicability of machine learning algorithms (artificial neural network and random forest) for precipitation data recovery using environmental variables, such as air pressure, temperature, humidity, and wind speed. The proposed model showed a high performance in detecting the missing precipitation occurrence with an accuracy of 80%. In addition, the prediction results from the models showed predictive ability with a correlation coefficient ranging from 0.5 to 0.7 and R2 values of 0.53. Although both algorithms performed similarly in estimating precipitation, ANN performed slightly better. Based on the results of this study, we expect that the machine learning algorithms can contribute to improving hydrological modeling performance by recovering missing precipitation data at observation stations. HIGHLIGHTS Missing precipitation data is recovered using ANN and RF algorithms.; Air humidity and air pressure have a high correlation with precipitation occurrence.; Both models have high performance in detecting the precipitation occurrence.; ANN model has better performance than the RF model for recovering daily precipitation data in South Korea.;

Background Diabetes mellitus is one of the most common chronic diseases worldwide, and cardiovascular disease is the leading cause of morbidity and mortality in diabetic patients. Diabetic cardiomyopathy (DCM) is a phenomenon characterized by a deterioration in cardiac function and structure, independent of vascular complications. Among many possible causes, the renin-angiotensin-aldosterone system and angiotensin II have been proposed as major drivers of DCM development. In the current study, we aimed to investigate the effects of pharmacological activation of angiotensin-converting enzyme 2 (ACE2) on DCM. Methods The ACE2 activator diminazene aceturate (DIZE) was administered intraperitoneally to male db/db mice (8 weeks old) for 8 weeks. Transthoracic echocardiography was used to assess cardiac mass and function in mice. Cardiac structure and fibrotic changes were examined using histology and immunohistochemistry. Gene and protein expression levels were examined using quantitative reverse transcription polymerase chain reaction and Western blotting, respectively. Additionally, RNA sequencing was performed to investigate the underlying mechanisms of the effects of DIZE and identify novel potential therapeutic targets for DCM. Results Echocardiography revealed that in DCM, the administration of DIZE significantly improved cardiac function as well as reduced cardiac hypertrophy and fibrosis. Transcriptome analysis revealed that DIZE treatment suppresses oxidative stress and several pathways related to cardiac hypertrophy. Conclusion DIZE prevented the diabetes mellitus-mediated structural and functional deterioration of mouse hearts. Our findings suggest that the pharmacological activation of ACE2 could be a novel treatment strategy for DCM.

Abstract Structured illumination microscopy (SIM) has become the standard for next-generation wide-field microscopy, offering ultrahigh imaging speed, superresolution, a large field-of-view, and long-term imaging. Over the past decade, SIM hardware and software have flourished, leading to successful applications in various biological questions. However, unlocking the full potential of SIM system hardware requires the development of advanced reconstruction algorithms. Here, we introduce the basic theory of two SIM algorithms, namely, optical sectioning SIM (OS-SIM) and superresolution SIM (SR-SIM), and summarize their implementation modalities. We then provide a brief overview of existing OS-SIM processing algorithms and review the development of SR-SIM reconstruction algorithms, focusing primarily on 2D-SIM, 3D-SIM, and blind-SIM. To showcase the state-of-the-art development of SIM systems and assist users in selecting a commercial SIM system for a specific application, we compare the features of representative off-the-shelf SIM systems. Finally, we provide perspectives on the potential future developments of SIM.

People in situations where physical activity is difficult face limitations in participating in exercise to maintain health. Participation in exercise is especially difficult when social non-face-to-face situations such as infectious diseases occur. This causes various social problems. Background: The study developed a 3D virtual reality (VR) Motionless Imagery Exercise through Avatar (MIEA), provided customized content such as companion avatars, and aimed to verify its effects. Methods: This study was conducted using a randomized block design experiment. The participants were 40 healthy adults (20 experiment and 20 control group) aged between 19 and 49 years. Both groups engaged in interventions three times per week for 20 min over six weeks. Data obtained from measurements of emotional well-being, cognition, and physiological responses before and after the experiment were analyzed. Results: The experimental group showed significant improvements in emotional well-being compared to the control group, particularly in trait anxiety, resilience, stress, and positive affect. The similar physiological responses observed in the experimental group resembled exercise-induced responses. Verbal memory and working memory in the experimental group improved more in the experimental group than the control group. Conclusions: The results showed the effectiveness of a 3D VR MIEA, indicating its positive impact on exercise outcomes. Furthermore, the provision of customized content including companion avatars was validated to enhance emotional well-being. This suggests that incorporating content-driven companion avatars in developing virtual reality exercise programs can evoke emotional effects.

Gerstmann-Sträussler-Scheinker (GSS) disease is a rare hereditary prion disease which is clinically characterized by a progressive cerebellar ataxia followed by cognitive impairment. We report a rare case of GSS disease in a 39-year-old male patient who complained of a progressive gait disturbance followed by dysarthria with cognitive impairment, after five months from the onset of initial symptom. His brain MRI scan revealed multifocal symmetric diffusion restricted lesions with T2/FLAIR hyperintensities in bilateral cerebral cortices, basal ganglia, and thalami. His family members also manifested similar symptoms in their 40–50s, suggesting the possibility of a genetic disease. Finally, he was genetically diagnosed with GSS disease by real-time quaking-induced conversion and prion protein (PRNP) gene sequencing test.

Abstract The conducting polymer poly(3,4-ethylenedioxythiophene) (known as PEDOT) is routinely fabricated into doped thin films for investigation of its inherent properties as well as for a range of applications. Fabrication of PEDOT is often achieved via oxidative polymerisation, where the conducting polymer is polymerised and doped (oxidised) to yield a conductive polymer thin film. The oxidiser and the polymerisation temperature are two parameters that may influence the properties and performance of the resultant PEDOT thin film. In this study, the role of temperature for the chemical polymerisation of PEDOT using the oxidiser iron tosylate is investigated from a computational and experimental viewpoint. While computations of the doping energetics suggest increasing doping with increasing temperature, x-ray photoelectron spectroscopy of fabricated PEDOT thin films indicate doping is much more complicated. With the aid of computations of the spatial distribution functions for tosylate in PEDOT, experiments indicate that two different populations of tosylate anions exist in the PEDOT matrix. Their relative populations change as a function of the polymerisation temperature. Therefore, polymerisation temperature plays a critical role in tailoring the properties of PEDOT in pursuit of being fit-for-purpose for the desired application.

This study aimed to plan an alternative for community street lighting in an older community by simulating illuminance improvements. We applied the natural surveillance principle of crime prevention through environmental design to an older community in Busan Metropolitan City in South Korea. We conducted four field investigations to identify lighting sources and measure their illuminance and heights. Using the Relux Pro program, the gaps in lighting were identified and alternative plans for improvement for night lighting were simulated. Narrow alleys and houses were sources of light disruption and lighting blind spots. We determined the location and type of lighting within the community and considered the continuity necessary to meet natural surveillance standards in alternative settings. We considered visibility, facial recognition, the risk of traffic accidents, and other variables (i.e., lamp type). Our results confirmed that the community’s average horizontal illuminance met the requirement of the Korean Agency for Technology and Standards and the minimal illuminance criterion of the International Commission on Illumination in all community lighting spaces—which was improved by about 2.2% to 85.7% compared to the previous situation. The results of this study are meaningful in that they present an effective planning support tool using simulation methods to establish community street lighting alternatives and determine their suitability.

Abstract Quantitative susceptibility mapping (QSM) for 61 patients with dissecting intramural hematomas (n = 36) or atherosclerotic calcifications (n = 25) in intracranial vertebral arteries were collected to assess intra- and interobserver reproducibility in a 3.0-T MR system between January 2015 and December 2017. Two independent observers each segmented regions of interest for lesions twice. The reproducibility was evaluated using intra-class correlation coefficients (ICC) and within-subject coefficients of variation (wCV) for means and concordance correlation coefficients (CCC) and ICC for radiomic features (CCC and ICC > 0.85) were used. Mean QSM values were 0.277 ± 0.092 ppm for dissecting intramural hematomas and − 0.208 ± 0.078 ppm for atherosclerotic calcifications. ICCs and wCVs were 0.885–0.969 and 6.5–13.7% in atherosclerotic calcifications and 0.712–0.865 and 12.4–18.7% in dissecting intramural hematomas, respectively. A total of 9 and 19 reproducible radiomic features were observed in dissecting intramural hematomas and atherosclerotic calcifications, respectively. QSM measurements in dissecting intramural hematomas and atherosclerotic calcifications were feasible and reproducible between intra- and interobserver comparisons, and some reproducible radiomic features were demonstrated.

Abstract Background We aimed to investigate the gut microbiota of patients with established rheumatoid arthritis (RA) who have been managed with disease-modifying anti-rheumatic drugs (DMARDs) for a long time. We focused on factors that might affect composition of the gut microbiota. Furthermore, we investigated whether gut microbiota composition predicts future clinical responses to conventional synthetic DMARDs (csDMARDs) in patients with an insufficient response to initial therapy. Methods We recruited 94 patients with RA and 30 healthy participants. Fecal gut microbiome was analyzed by 16S rRNA amplificon sequencing; the resulting raw reads were processed based on QIIME2. Calypso online software was used for data visualization and to compare microbial composition between groups. For RA patients with moderate-to-high disease activity, treatment was changed after stool collection, and responses were observed 6 months later. Results The composition of the gut microbiota in patients with established RA was different from that of healthy participants. Young RA patients (

Plasmonic nanostructures are emerging as a promising avenue for nanophotonics due to their extreme light and thermal confinement, ultrafast manipulation processes, and potential uses in device miniaturization. However, their fixed functions have limited their versatility in applications. This review provides an overview of recent switchable plasmonic nanostructure engineering techniques, focusing on methods that provide reversible switchability. Passive optical switching, active structure‐tunable switching, active material‐based switching, and advanced applications, such as multifunctional biomedical sensing, energy harvesting, and dynamic optical devices, are discussed. The specific methods and techniques used to engineer switchable plasmonic nanostructures are also highlighted. By understanding the latest developments and overall trends, this review is expected to help researchers design and fabricate advanced plasmonic nanostructures with unprecedented switchability and versatility for various applications.

Accurate prediction of dam inflows is essential for effective water resources management in terms of both water quantity and quality. This study aims to develop a Long Short-Term Memory (LSTM) deep learning-based monthly dam inflow prediction model using large-scale climate indices. Six climate indices, Atlantic multidecadal oscillation (AMO), El Niño–southern oscillations (ENSO), North Atlantic oscillation (NAO), Pacific decadal oscillation (PDO), Niño 3.4, and Southern Oscillation Index (SOI) for the period of 1981–2020, were used as input variables of the model. The proposed model was trained with 29 years of data (1981–2009) and tested with 12 years of data (2009–2020). We investigated 29 input data combinations to evaluate the predictive performance according to different input datasets. The model showed the average values of metrics ranged from 0.5 to 0.6 for CC and from 40 to 80 cm for root mean square error (RMSE) at three dams. The prediction results from the model showed lower performance as the lead time increased. Also, each dam showed different prediction results for different seasons. For example, Soyangriver/Daecheong dams have better accuracy in prediction for the wet season than the dry season, whereas the Andong dam has a high prediction ability during the dry season. These investigations can be used for better efficient dam management using a data-driven approach. HIGHLIGHTS A dam inflow prediction model was developed using the LSTM-based deep learning method and climate indices.; Six climate indices including AMO, ENSO, NAO, PDO, Niño 3.4, and SOI are considered as input variables.; The proposed model tests 29 different input combinations to find the best combinations for prediction.; The proposed model shows the applicability to predict dam inflow variability in different locations.;

This article proposes a statistical shmoo for a Decision Feedback Equalizer (DFE). A statistical shmoo shows a Bit-Error Rate (BER) distribution of a system depending on conditions such as a threshold voltage and sampling time. The previous statistical eye diagram can be converted to a statistical shmoo except for the case of a DFE. In the shmoo, rows and columns represent different threshold voltages and sampling times, respectively. Thus, the DFE operation also depends on each row and column. Furthermore, a DFE may have error propagations because of substantial noise. Therefore, this article proposes a statistical shmoo for an ideal and a nonideal DFE. The ideal DFE always makes a correct bit decision, whereas the nonideal DFE may make a wrong bit decision depending on the received waveform. For verification, the obtained statistical shmoo is compared to the transient simulation with their BER values. The lowest BER value achieved by the proposed method is 1E-51 with a calculation time of 862 seconds. While the transient simulation achieved the BER of 8.26E-8 with a simulation time of 2,000,000 seconds. In conclusion, the proposed method provides an accurate statistical shmoo in efficient manner.

This article proposes an accelerated statistical eye diagram estimation method for efficient signal integrity (SI) analysis. An eye diagram is a critical metric in the SI analysis, however obtaining an eye diagram is time-consuming in simulation. The required time might be a few weeks depending on the complexity. Because the eye diagram is obtained by superposition of the received waveforms. Statistical eye diagram estimation methods were proposed to make the eye diagram acquisition efficient, however, it still has a limited improvement due to a large number of convolution operations. To address this limitation, the proposed method includes two approaches: ( $i$ ) an increased voltage step in the amplitude PDF with a compensation factor and (ii) skippable and replaceable convolution operations. The efficiency of the proposed method is verified by comparing the derived result with that of a conventional transient simulation. The previous estimation method requires 1,065 s while approaches ( $i$ ) and (ii) reduce the required time to 88 and 510 s, respectively. When both approaches are applied, the required time is reduced to 17 s in comparison to the simulation time of 259,200 s required for a transient simulation. Approach (ii) reduces the number of convolution operations from 768,000 to 103,000. Therefore, the proposed method successfully reduces the required simulation time and the required number of convolutions to obtain a statistical eye diagram.

Given the global competitive landscape, it is imperative that businesses maintain and manage their facilities continuously to enhance efficiency and productivity for sustaining competitiveness. Hence, a new hybrid model called contrast enhancement convolutional vision transformer (CECvT) was developed in this study that enables fault diagnosis without physical contact with factory equipment to ensure accurate initial fault detection without the risk of machine damage or interference. This model leverages thermal imaging as an apt source for early anomaly detection in equipment. A new contrast enhancement module employing contrast enhancement techniques was integrated to address the edge information loss when utilizing thermal images. Moreover, the network performance was enhanced by fusing the advantages of convolutional neural network (CNN) and Transformer models. Notably, the model design allows deriving detailed feature information necessary for the initial diagnostics by harnessing multiscale information to extract and concatenate features. The proposed method’s performance was evaluated using the thermal imaging dataset provided by AI Hub. When juxtaposed with CNN, Transformer, and hybrid CNN–Transformer models, the proposed model demonstrated a superior accuracy of 96.17%. Furthermore, it achieved the most accurate diagnosis at the inception of abnormalities than the other networks. The proposed model thus has potential and is preferrable for various thermal-imaging-based fault diagnosis applications owing to its excellent performance and precision during initial diagnosis.

A novel method of enhancing radiation performance is demonstrated by applying a hybrid reflector composed of inhomogeneous metamaterial elements to a four-arm log-periodic toothed antenna. The operation of the antennas by various metamaterial cells is observed, and the causes of the radiation performance deterioration and narrow bandwidth are investigated through the field distribution analysis. The metamaterial cells are arranged in suitable positions under the antenna for good radiation performance and wide bandwidth according to AMC and EBG characteristics. In the proposed hybrid reflector, surface wave suppression characteristics are used for impedance stabilization by placing the I-shaped EBG cell under the inactive region of the antenna. The performance of the fabricated antenna with dual linear polarization capability is verified by measuring co- and cross-polarization radiation patterns. The newly proposed hybrid reflector for the four-arm log-periodic toothed antenna represents an over 4 dBi realized gain within an impedance ratio bandwidth of over 11.6:1.

Vision-based autonomous driving is rapidly growing. There are, however, presently no agreed-upon metrics for assessing how well deep neural network (DNN) models perform in driving. To compare novel approaches and architectures to existing ones, some researchers employed a mean error between labeled and predicted values in a test dataset and others presented a new metric that is designed to match their requirements. The discrepancy in the usage of various performance metrics and lack of objective metrics to judge the driving performance were our primary motives for developing a feasible solution. In this study, we propose online performance evaluation metrics index (OPEMI), an integrated metric that can evaluate the driving capabilities of autonomous driving models in various driving scenarios. To evaluate driving performance precisely and objectively, OPEMI incorporates several variables, including driving control stability, driving trajectory stability, journey duration, travel distance, success rate, and speed. To demonstrate the validity of OPEMI, we first confirmed that the prediction accuracy has a weak correlation with driving performance. Then, we have discussed the constraints in the existing driving performance metrics in certain circumstances, and their failure to assess the driving models. Finally, we conducted experiments with four popular DNN models and two in-house models under three different driving scenarios (generic, urban, and racing). The results show that the proposed evaluation metric, OPEMI, realistically displays driving performance and demonstrates its validity in various driving scenarios.

Computed tomography (CT) is a rapid and precise medical imaging modality, but it poses the challenge of high radiation exposure to patients. To control this issue, stringent quality control (QC) evaluations are imperative for CT. One crucial aspect of CT QC involves the evaluation of phantom images, utilizing specifically designed phantoms for accuracy management and subsequent objective evaluation. However, CT QC has qualitative evaluation methods, particularly for evaluating spatial and contrast resolutions. To solve this problem, we propose a quality control method based on deep-learning object detection for quantitatively evaluating spatial and contrast resolutions, CT Attention You Only Look Once v8 (CTA-YOLOv8). First, we utilized the YOLOv8 network as the foundational model, optimizing it for enhanced accuracy. Second, we enhanced the network’s capabilities by integrating the Convolutional Block Attention Module (CBAM) and Swin Transformers, tailored for phantom image evaluations. The CBAM module was employed internally to pinpoint the optimal position for achieving peak performance in CT QC data. Similarly, we fine-tuned the code and patch size of the Swin Transformer module to align it with YOLOv8, culminating in the identification of the optimal configuration. Our proposed CTA-YOLOv8 network showed superior agreement with qualitative evaluation methods, achieving accuracies of 92.03% and 97.56% for spatial and contrast resolution evaluations, respectively. Thus, we suggest that our method offers nearly equivalent performance to qualitative methods. The utilization of the CTA-YOLOv8 network in evaluating CT phantom images holds potential for setting a new standard in quantitative assessment methodologies.

The “oxygen effect” improves radiation efficacy; thus, tumor cell oxygen concentration is a crucial factor for improving lung cancer treatment. In the current study, we aimed to identify aerobic exercise-induced changes in oxygen concentrations in non-small cell lung cancer (NSCLC) cells. To this end, an NSCLC xenograft mouse model was established using human A549 cells. Animals were subsequently subjected to aerobic exercise and radiation three times per week for 2 weeks. Aerobic exercise was performed at a speed of 8.0 m/m for 30 min, and the tumor was irradiated with 2 Gy of 6 MV X-rays (total radiation dose 12 Gy). Combined aerobic exercise and radiation reduced NSCLC cell growth. In addition, the positive effect of aerobic exercise on radiation efficacy through oxygenation of tumor cells was confirmed based on hypoxia-inducible factor-1 and carbonic anhydrase IX expression. Finally, whole-transcriptome analysis revealed the key factors that induce oxygenation in NSCLC cells when aerobic exercise was combined with radiation. Taken together, these results indicate that aerobic exercise improves the effectiveness of radiation in the treatment of NSCLC. This preclinical study provides a basis for the clinical application of aerobic exercise to patients with NSCLC undergoing radiation therapy.

Due to the nature of the industrial control systems (ICS) environment, where process continuity is essential, intentionally initiating a cyberattack to check security controls can cause severe financial and human damage to the organization. Therefore, most organizations operating ICS environments check their level of security through simulated cybersecurity exercises. For these exercises to be effective, high-quality cyberattack scenarios that are likely to occur in the ICS environment must be assumed. Unfortunately, many organizations use limited attack scenarios targeting essential digital assets, leading to ineffective response preparedness. To derive high-quality scenarios, there is a need for relevant attack and vulnerability information, and standardized methods for creating and evaluating attack scenarios in the ICS context. To meet these challenges, we propose GENICS, an attack scenario generation framework for cybersecurity training in ICS. GENICS consists of five phases: threat analysis, attack information identification, modeling cyberattack scenarios, quantifying cyberattacks, and generating scenarios. The validity of GENICS was verified through a qualitative study and case studies on current attack scenario-generating methods. GENICS ensures a systematic approach to generate quantified, realistic attack scenarios, thereby significantly enhancing cybersecurity training in ICS environments.

Accurate prediction of postnatal growth failure (PGF) can be beneficial for early intervention and prevention. We aimed to develop a machine learning model to predict PGF at discharge among very low birth weight (VLBW) infants using extreme gradient boosting. A total of 729 VLBW infants, born between 2013 and 2017 in four hospitals, were included. PGF was defined as a decrease in z-score between birth and discharge that was greater than 1.28. Feature selection and addition were performed to improve the accuracy of prediction at four different time points, including 0, 7, 14, and 28 days after birth. A total of 12 features with high contribution at all time points by feature importance were decided upon, and good performance was shown as an area under the receiver operating characteristic curve (AUROC) of 0.78 at 7 days. After adding weight change to the 12 features—which included sex, gestational age, birth weight, small for gestational age, maternal hypertension, respiratory distress syndrome, duration of invasive ventilation, duration of non-invasive ventilation, patent ductus arteriosus, sepsis, use of parenteral nutrition, and reach at full enteral nutrition—the AUROC at 7 days after birth was shown as 0.84. Our prediction model for PGF performed well at early detection. Its potential clinical application as a supplemental tool could be helpful for reducing PGF and improving child health.

Abstract Artificial intelligence (AI) is limited to teeth and periodontal disease in the dental field, and is used for diagnosis assistance or data analysis, and there has been no research conducted in actual clinical situations. So, we created an environment similar to actual clinical practice and conducted research by selecting three of the soft tissue diseases (carotid artery calcification, lymph node calcification, and sialolith) that are difficult for general dentists to see. Therefore, in this study, the accuracy and reading time are evaluated using panoramic images and AI. A total of 20,000 panoramic images including three diseases were used to develop and train a fast R-CNN model. To compare the performance of the developed model, two oral and maxillofacial radiologists (OMRs) and two general dentists (GDs) read 352 images, excluding the panoramic images used in development for soft tissue calcification diagnosis. On the first visit, the observers read images without AI; on the second visit, the same observers used AI to read the same image. The diagnostic accuracy and specificity for soft tissue calcification of AI were high from 0.727 to 0.926 and from 0.171 to 1.000, whereas the sensitivity for lymph node calcification and sialolith were low at 0.250 and 0.188, respectively. The reading time of AI increased in the GD group (619 to 1049) and decreased in the OMR group (1347 to 1372). In addition, reading scores increased in both groups (GD from 11.4 to 39.8 and OMR from 3.4 to 10.8). Using AI, although the detection sensitivity of sialolith and lymph node calcification was lower than that of carotid artery calcification, the total reading time of the OMR specialists was reduced and the GDs reading accuracy was improved. The AI used in this study helped to improve the diagnostic accuracy of the GD group, who were not familiar with the soft tissue calcification diagnosis, but more data sets are needed to improve the detection performance of the two diseases with low sensitivity of AI.

Abstract Background & objective The ongoing coronavirus disease 2019 (COVID-19) pandemic continues to cause death and socioeconomic problems worldwide. This study examined the spatial distribution of social vulnerability to COVID-19 and its relationship with the number of confirmed COVID-19 cases in 2020, focusing on the Capital region of South Korea. Methods A traditional social vulnerability index (SVI), healthy SVI, and the difference of each SVI were constructed in 2015 and 2019. The traditional SVI was constructed across five domains: age, socioeconomic disadvantage, housing, income, and environment. The healthy SVI domains were: prevention, health-related habits, chronic disease, healthcare infrastructure, and mortality. The spatial distribution of the traditional SVI, healthy SVI, and confirmed cases of COVID-19 was explored using ArcGIS 10.5. Pearson correlation was used to identify the relationship between confirmed COVID-19 cases and the two SVIs and their changes between 2015 and 2019. Four multiple linear regression models were used to identify the impact of the changes of the two SVIs on the confirmed COVID-19 cases for the three episodes and total period with control of population using STATA/MP 16.1. Results Confirmed COVID-19 cases were concentrated in a specific area of the Capital region. The traditional SVI was more vulnerable in the outer regions of the Capital region, and some central, western, and eastern areas reflected an increase in vulnerability. Healthy SVI was more vulnerable in the northern part of the Capital region, and increase in vulnerability showed in some central areas above Seoul. By multiple regression with the population controlled, the difference of the traditional SVI between 2015 and 2019 showed a positive relationship with the confirmed COVID-19 cases in all models at a significance level of 0.05, and the 2019 integrated SVI showed a negative relationship with confirmed COVID-19 cases in all models. Conclusions The results of this study showed that the confirmed COVID-19 cases are associated with increased traditional SVI vulnerability between 2015 and 2019 and have a high positive relationship with the spread of COVID-19. Policy efforts are needed to reduce confirmed COVID-19 cases among the vulnerable in regions with relatively increased traditional SVI.

Abstract Laser direct-writing enables micro and nanoscale patterning, and is thus widely used for cutting-edge research and industrial applications. Various nanolithography methods, such as near-field, plasmonic, and scanning-probe lithography, are gaining increasing attention because they enable fabrication of high-resolution nanopatterns that are much smaller than the wavelength of light. However, conventional methods are limited by low throughput and scalability, and tend to use electron beams or focused-ion beams to create nanostructures. In this study, we developed a procedure for massively parallel direct writing of nanoapertures using a multi-optical probe system and super-resolution near-fields. A glass micro-Fresnel zone plate array, which is an ultra-precision far-field optical system, was designed and fabricated as the multi-optical probe system. As a chalcogenide phase-change material (PCM), multiple layers of Sb65Se35 were used to generate the super-resolution near-field effect. A nanoaperture was fabricated through direct laser writing on a large-area (200 × 200 mm2) multi-layered PCM. A photoresist nanopattern was fabricated on an 8-inch wafer via near-field nanolithography using the developed nanoaperture and an i-line commercial exposure system. Unlike other methods, this technique allows high-throughput large-area nanolithography and overcomes the gap-control issue between the probe array and the patterning surface.

Abstract Background Microplastics (MPs) and nanoplastics (NPs) formed from decomposed plastic are increasing environmental threats. Although MPs and NPs exposed through various routes enter the systemic circulation, the potential toxicity of those is largely unknown. We investigated whether polystyrene NPs (PS-NPs) promote the coagulation activity of red blood cells (RBCs). Results We tested several types of PS-NPs using human RBCs and found that amine-modified 100 nm PS-NPs were the most potent. We measured the uptake of PS-NPs using flow cytometry and confocal microscopy. Electron microscopy revealed morphological changes of RBCs by PS-NPs. PS-NPs induced the externalization of phosphatidylserine, generation of microvesicles in RBCs, and perturbations in the intracellular microenvironment. PS-NPs increased the activity of scramblases responsible for phospholipid translocation in RBCs. PS-NPs modulated the functional interaction to adjacent tissues and coagulation cascade, enhancing RBC adhesion and thrombin generation. Our observations in human RBCs were consistent with those in isolated rat RBCs, showing no inter-species differences. In rat venous thrombosis models, the intravenous administration of PS-NPs enhanced thrombus formation. Conclusion Amine-modified PS-NPs induce the prothrombotic activation of RBCs causing thrombus formation. We believe that our study will contribute to understanding the potential toxicity of amine-modified polystyrene particles in blood cells and cardiovascular systems.

The long-term maintenance of diverse salivary gland cells remains challenging. Here the authors establish a protocol for long-term maintenance of murine and human salivary gland organoids exhibiting gland-specific gene expression, gland functions, and cellular diversity confirmed by scRNA-seq.

As the novel coronavirus disease (COVID-19) has been rapidly spreading across the world, scholars have started paying attention to risk factors that affect the occurrence of the infectious disease. While various urban characteristics have been shown to influence the outbreak, less is known about whether COVID-19 is more likely to be transmitted in areas with a greater number of incidents of previous infectious diseases. This study examines a spatial relationship between COVID-19 and previous infectious diseases from a spatial perspective. Using the confirmed cases of COVID-19 and other types of infectious diseases across South Korea, we identified spatial clusters through regression and spatial econometric models. We found that COVID-19-confirmed case rates tended to be clustered despite no similarity with the spatial patterns of previous infectious diseases. Existing infectious diseases from abroad were associated with the occurrence of COVID-19, while the effect diminished after controlling for the spatial effect. Our findings highlight the importance of regional-level infectious disease surveillance for the effective prevention and control of COVID-19.

Drought has significant impacts on both society and the environment, but it is a gradual and comprehensive process that affects a region over time. Therefore, non-structural measures are necessary to prepare and respond to the damage caused by drought in a flexible manner according to the stage of drought. In this study, an AI-based water demand prediction model was developed using deep neural network (DNN) and long short-term memory (LSTM) models. The model was trained from 2004 to 2015 and verified from 2016 to 2021. Model accuracy was evaluated using data, with the LSTM model achieving a correlation coefficient (CC) of 0.95 and normalized root mean square error (NRMSE) of 8.38, indicating excellent performance. The probability of the random variable X falling within the interval [a,b], as described by the probability density function f(x), was calculated using the water demand data. The cumulative distribution function was used to calculate the probability of the random variable being less than or equal to a specific value. These calculations were used to establish the criteria for each stage of the crisis alert system. Decision tree (DT) and random forest (RF) models, based on AI-based classification, were used to predict water demand at the Gurye intake station. The models took into account the impact of water demand from the previous day, as well as the effects of rainfall, maximum temperature, and average temperature. Daily water demand data from the Gurye intake station and the previous day’s rainfall, maximum temperature, and average temperature data from a nearby observatory were collected from 2004 to 2021. The models were trained on data from 2004 to 2015 and validated on data from 2016 to 2021. Model accuracy was evaluated using the F1-score, with the random forest model achieving a score of 0.88, indicating excellent performance.

So far, cymbal transducers have been developed primarily for transmitting purposes, and even when used for receiving, the focus has been mostly on improving the receiving sensitivity. In this study, we developed a cymbal hydrophone with a higher sensitivity and a wider bandwidth than other existing hydrophones. First, the initial structure of the cymbal hydrophone was established, and then the effects of structural variables on the hydrophone’s performance were analyzed using the finite element method. Based on the analysis results, the structure having the highest sensitivity and widest bandwidth, with a receiving voltage sensitivity level above a certain threshold, was derived using optimal design techniques. A prototype of the cymbal hydrophone with the designed structure was fabricated, and its performance was measured, validating the effectiveness of the design by comparing the measurement results with the design values. The developed cymbal hydrophone is expected to be utilized in various underwater precision measurements, as it possesses a significantly broader reception frequency bandwidth when compared with other hydrophones used for the same purpose.

Minimum housing standards aim to safeguard housing rights and enhance residential conditions. Governments develop housing policies, including provision, preservation, and support for welfare programs, guided by the status of households below these standards. Growing nations commonly utilize this approach to decrease the proportion of households not meeting these criteria. In South Korea, the ratio of households below these standards was 4.5% in 2021, down from 16.6% in 2006, as indicated by the initial Korea Housing Survey. However, this downward trend has stalled over the past decade. With the 2004 and 2011 revisions, the standards have been effective for 12 years, yet no longer mirror current housing realities due to ongoing improvements. This study aimed to propose enhancements to Korea’s minimum housing standards. Through analyzing laws, prior research, present household statuses, international cases, and expert insights, recommendations emerged. Categorizing households by size, we developed precise standards covering area, facility, and location aspects. These new standards led to an 8.4% non-compliance rate in 2021. This research’s findings anticipate aiding the revision of minimum housing standards, formulating pragmatic policies for enhancing residential conditions in line with present situations.

Surface plasmon resonance microscopy (SPRM) combines the principles of traditional microscopy with the versatility of surface plasmons to develop label-free imaging methods. This paper describes a proof-of-principles approach based on deep learning that utilized the Y-Net convolutional neural network model to improve the detection and analysis methodology of SPRM. A machine-learning based image analysis technique was used to provide a method for the one-shot analysis of SPRM images to estimate scattering parameters such as the scatterer location. The method was assessed by applying the approach to SPRM images and reconstructing an image from the network output for comparison with the original image. The results showed that deep learning can localize scatterers and predict other variables of scattering objects with high accuracy in a noisy environment. The results also confirmed that with a larger field of view, deep learning can be used to improve traditional SPRM such that it localizes and produces scatterer characteristics in one shot, considerably increasing the detection capabilities of SPRM.

Lubricant (or oil)-impregnated porous surface has been considered as a promising surface treatment to realize multifunctionality. In this study, silicone oil was impregnated into a hard porous oxide layer created by the plasma electrolytic oxidation (PEO) of aluminum (Al) alloys. The monolayer of polydimethylsiloxane (PDMS) from silicone oil is formed on a porous oxide layer; thus, a water-repellent slippery oil-impregnated surface is realized on Al alloy, showing a low contact angle hysteresis of less than 5°. This water repellency significantly enhanced the corrosion resistance by more than four orders of magnitude compared to that of the PEO-treated Al alloy without silicone oil impregnation. The silicone oil within the porous oxide layer also provides a lubricating effect to improve wear resistance by reducing friction coefficients from ~0.6 to ~0.1. In addition, because the PDMS monolayer can be restored by frictional heat, the water-repellent surface is tolerant to physical damage to the oxide surface. Hence, the results of this fundamental study provide a new approach for the post-treatment of PEO for Al alloys.