This study delves into the essential characteristics of the Metaverse and its impact on the real world. The Metaverse, a parallel universe composed of countless 3D virtual worlds, has become a hot topic in technology. With the development of advanced technologies such as Virtual Reality (VR), Augmented Reality (AR), blockchain, and Artificial Intelligence, the Metaverse is gradually changing people's lifestyles and work methods, expanding the boundaries of the digital economy. This study profoundly investigates the attributes of the Metaverse, such as asset digitization, application scenario-based, scope economy, industry universality, integrated innovation, and the platform ecosystem. Additionally, we propose a theoretical framework to aid in understanding and shaping this emerging field. We conducted a thorough study and analysis of a specific case, explored the key features of the Metaverse, unveiled the profound impact of Metaverse characteristics on social and economic development, and verified our theoretical framework with this case. This research provides new perspectives and methods for academic studies and offers robust support and guidance for practical applications and decision-making.

With the widespread Application of Virtual Reality (VR) in education, optimizing foreign language learning in VR has become a focal point of research. This paper introduces a comprehensive theoretical framework (VR-CCL) based on constructivism and cognitive load theory to enhance foreign language learning in VR. Through a literature review, we explore the applications of VR in education, foreign language learning theories, and prior works on technology-assisted language learning. We further detail the three main components of the VR-CCL framework and validate its effectiveness through two case studies: Duolingo VR and Rosetta Stone VR. Finally, we discuss the strengths and limitations of the framework and its implications for educators and developers.

This study introduces the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), an innovative machine learning-based approach to personalize educational pathways. DFDLOF leverages advanced algorithms for in-depth analysis of learner data, enabling the dynamic customization of educational content and methodologies. Its application in diverse online platforms, including Khan Academy and Coursera, underscores its adaptability and scalability. DFDLOF enhances learner engagement and outcomes by providing tailored learning experiences and real-time feedback. Key insights reveal its strengths in personalized education, particularly in dynamically adapting learning paths and the importance of real-time feedback. Challenges identified include integration complexities and data privacy concerns. Future research directions emphasize the need for longitudinal impact studies and broader demographic applications. This study contributes significantly to the evolving domain of adaptive learning technologies, demonstrating the transformative potential of machine learning in personalizing education.

This research presents the Cross-Cultural Intelligent Language Learning System (CILS), a novel approach integrating Artificial Intelligence (AI) into language education to enhance cross-cultural communication. CILS utilizes advanced AI technologies to provide adaptive, personalized learning experiences that cater to the unique linguistic and cultural backgrounds of each learner. By dynamically adjusting content and methodology, CILS significantly improves linguistic proficiency and cultural understanding, essential for effective global interactions. The implementation of CILS in platforms such as Busuu and HelloTalk has demonstrated marked improvements in engagement and communication skills among learners. Empirical studies validate the system's effectiveness in real-world settings, showing enhanced learner performance and increased intercultural competence. Additionally, the Technology Acceptance Model (TAM) applied confirms that the usability and perceived usefulness of AI-driven systems strongly influence learner acceptance and sustained use. This study not only underscores the potential of AI in transforming language education but also highlights the critical role of cultural sensitivity in designing educational technologies.

With the rapid spread of carbon trading in the global economy, the interactions of prices between carbon (or clean/renewable energy) and traditional fossil energies such as coal and oil have raised growing attention, but little research have discussed their dynamic volatility spillover and time-varying correlation. The purpose of this study is to investigate these issues, for the weekly data of EUA futures, Biofuel and Brent oil prices from 25 October 2009 to 5 July 2020. We employ the VAR-GARCH model with the BEKK specification. Our results are summarized as follows. At first, we identified the sudden changes and the volatility persistence in the three markets, and also confirmed that the volatility of the markets has changed significantly over time. Secondly, we find that there are a weak volatility spillover effect among the three markets, while a strong spillover effect between the EUA and Brent oil markets. Lastly, in financial markets, the EUA can be used as a hedging portfolio for the Biofuel and Brent oil markets. These results can help investors to well compose their portfolios and manage their investment risks, and help potential pollutant emission sources to join in carbon market in a cost-effective way.

Changes in crude oil price affect the shipping freight market in three different channels. This study explores the dependence structure between oil prices and maritime freight rates to identify the strongest channel. Therefore, it investigates the relationship between oil prices and three major maritime freight rates; the Baltic Dry Index (BDI), the Baltic Dirty Tanker Index (BDTI), and the Baltic Clean Tanker Index (BCTI). We employ the decomposition method, not studied in the existing literature. The copula approach identifies the time-varying effects and asymmetry in the tail dependence structure between oil prices and freight rates. The main results of this analysis are as follows. The decomposed components display different conditional dependence patterns, and asymmetry is revealed in the upper and lower tail dependence. In the long run, we find more dependence in extreme periods like the financial crises. In short-run fluctuations, we find the dependence increases in an economic boom. The implications of the results suggest that dependence can vary over time and may change depending on extreme events, implying that the complementary strategies of the long run and short run should be different.

We analyze the upside and downside risk connectedness among international stock markets. We characterize the connectedness among international stock returns using the Diebold and Yilmaz spillover index approach and compute the upside and downside value-at-risk. We document that the connectedness level of the downside risk is higher than that of the upside risk and that stock markets are more sensitive when the stock market declines. We also find that specific periods (e.g., the global financial crisis, the European debt crisis, and the COVID-19 turmoil) intensify the spillover effects across international stock markets. Our results demonstrate that the EU, Ger-many, and the US acted as net transmitters of dynamic connectedness; however, Japan (JP), China (CH), and India (IN) acted as net receivers of dynamic connectedness during the sample period. These findings provide significant new information to policymakers and market participants.

During the last two decades, energy poverty has captured a growing attention of researchers and policymakers due to its strong association with economic poverty and poor economic performance. This study uses a broad set of macro level indicators and makes the first attempt to measure energy poverty and its impact on economic growth of Pakistan over the period 1990 to 2017. In particular, our energy poverty indicator considers four main dimensions of energy poverty, namely, energy services, clean energy, energy governance and energy affordability. Our main results show that though the overall energy poverty has reduced in Pakistan during the selected sample period, the country shows an increasing dependence on polluted energy supply in order to meet its growing demand of energy. In second stage of the investigation, we test the neoclassical growth theory where we incorporate energy poverty along with human capital as source of economic growth. Our cointegration results reveal a strong relationship between energy poverty and economic growth that is also dynamically stable in short run. These strong negative linkages between energy poverty with economic growth for the sample economy complement the previous literature on the subject.

This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron’s structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Student–t distribution fits the cryptocurrency market’s price dynamics well.

This paper examines interlinkages and hedging opportunities between nine major cryptocurrencies for the period between 30 September 2015 and 4 June 2020, which notably includes the coronavirus disease 2019 (COVID-19) outbreak lasting from early 2020 through the end of the sample period. The results of dynamic conditional correlation (DCC) analysis using a minimum connectedness approach show a high degree of correlation between cryptocurrencies throughout the sample period. However, the correlations reach their minimum values during the COVID-19 pandemic, which indicates that cryptocurrencies acted as a hedge or safe haven during the stressful period of the COVID-19 pandemic. The weight of cryptocurrencies was significantly reduced and their hedging effectiveness varied greatly during the pandemic, which indicates that investors’ preferences changed during the COVID-19 period.

With continued technological innovation in the fields of mixed reality (MR), wearable-type MR devices, such as helmets, have been released and are frequently used in various fields, such as entertainment, training, and education. However, because each product has different parts and specifications in terms of the design and manufacturing process, users feel that the virtual objects overlaying real environments in MR are visualized differently depending on the scale and color used by the MR device. In this paper, we compare the effect of scale and color parameters on users’ perception in using different types of MR devices to improve MR experience. We conducted two experiments (scale and color), and our experimental study showed that the subjects who participated in the scale perception experiment clearly tended to underestimate virtual objects, compared with real objects, and overestimate color in MR environments. [MM1]Please confirm meaning.

This paper presents a two-stage hierarchical neural network using image classification and object detection algorithms as key building blocks for a system that automatically detects a potential design right infringement. This neural network is trained to return the Top-N original design right records that highly resemble the input image of a counterfeit. Design rights specify the unique aesthetic characteristics of a product. Due to the rapid change of trends, new design rights are continuously generated. This work proposes an Ensemble Neural Network (ENN), an artificial neural network model that aims to deal with a large amount of counterfeit data and design right records that are frequently added and deleted. At first, we performed image classification and objection detection learning per design right using the existing models with a proven track record of high accuracy. The distributed models form the backbone of the ENN and yield intermediate results aggregated at a master neural network. This master neural network is a deep residual network paired with a fully connected network. This ensemble layer is trained to determine the sub-models that return the best result for a given input image of a product. In the final stage, the ENN model multiples the inferred similarity coefficients to the weighted input vectors produced by the individual sub-models to assess the similarity between the test input image and the existing product design rights to see any sign of violation. Given 84 design rights and the sample product images taken meticulously under various conditions, our ENN model achieved average Top-1 and Top-3 accuracies of 98.409% and 99.460%, respectively. Upon introducing new design rights data, a partial update of the inference model was done an order of magnitude faster than the single model. ENN maintained a high level of accuracy as it scaled out to handle more design rights. Therefore, the ENN model is expected to offer practical help to the inspectors in the field, such as the customs at the border that deal with a swarm of products.

The widespread integration and growing dependence among currency, stock and commodity markets make these markets often very vulnerable to shocks and at risk of collapse at the same time. As a result, these trends threaten the sustainability of the entire financial system. In this study, we explore the spillovers and nonlinear dependences between the seven major foreign ex-change rates, crude oil and gold prices, a global stock price index, and oil and stock implied volatility indices as proxy variables for global risk factors by em-ploying directional spillover network approach. We also use multi-scale de-composition method and nonlinear causality test between these variables to capture multi-level relationships at short and long horizons. Major findings are summarized as follows. First, from the multi-scale decomposition analysis, we identify that Granger causality test results and the direction and strength of return spillovers change with the level of decomposition. Second, the results of nonlinear causality tests show variation in both the significance and direction of Granger causality relationships between the decomposed currency and other series at different timescales, especially for the decomposed oil, gold, and OVX series. Third, the measured directional spillover indices identify the EUR as the largest contributor of connectedness to the other series. The central role of the EUR is a net transmitter of connectedness to gold, oil, the GBP, JPY, and CHF.

We investigate the impact of air quality and weather on the equity returns of the Shenzhen Exchange. To capture the air quality and weather effects, we use dummy variables created by employing a moving average and moving standard deviation. The important results are as follows. First, in the whole sample period (2005–2019), we find that high air pollution and extremely high temperature have significant and negative influence on the equity returns. In the sub-period I (2005–2012), the 11-day model and 31-day model show that high air pollution have significant and negative impacts on the Shenzhen stock returns. Second, the results of the quantile regression show that high air pollution have significant and negative effects during bullish market phase, and extremely high temperature have significant and negative effects during bearish market phase. This implies that the air quality and weather effects are asymmetric. Third, the weather effect of the abnormal temperature on the stock returns is greater in severe bearish market. Whereas the effect of the air pollution on the stock returns is greater in the bullish market. Fourth, the least squares method underestimates the air quality and weather effects compared to the quantile regression method, suggesting that the quantile regression method is more suitable in analyzing these effects in a very volatile emerging market such as the Shenzhen stock market.

We investigate the impact of air quality and weather on the stock market returns of the Shenzhen Exchange. To capture the air quality and weather effects, we apply dummy variables generated by applying a moving average and moving standard deviation. Our study provides several interesting results. First, in the whole sample period (2005–2019), we find that high air pollution and extremely high temperature have significant and negative effects on the Shenzhen stock returns. In the sub-period I (2005–2012), the 11-day model and 31-day model show that high air pollution have significant and negative effects on the Shenzhen stock returns. Second, the results of the quantile regression show that high air pollution have significant and negative effects during bullish market phase, and extremely high temperature have significant and negative effects during bearish market phase. This implies that the air quality and weather effects are asymmetric. Third, the more the Shenzhen stock returns drop, the greater the effect of the abnormal temperature is. Whereas, the more the Shenzhen stock returns increase, the greater the effect of the abnormal air quality is. Fourth, the least squares method underestimates the air quality and weather effects compared to the quantile regression method, suggesting that the quantile regression method is more suitable in analysing these effects in a very volatile emerging market such as the Shenzhen stock market.

In this study, the dependence between Bitcoin (BTC) and economic policy uncertainty (EPU) of USA and China is estimated by applying latest methodology of quantile cross-spectral dependence. The findings indicate a positive return interdependence between BTC and EPU is high in short-term, and this dependence decreases as investment horizons increase from weekly to yearly. The information on above interdependence is also extracted by applying wavelet coherence analysis and the estimation results suggest that correlation between BTC and EPU is positive during short-term investment horizon. Furthermore, more diversification benefits of BTC can be obtained during USA-EPU as compared to China-EPU.

Melanocortins play crucial roles in regulating the stress response, inflammation, and skin pigmentation. In this review, we focus on the melanocortin 1 receptor (MC1R), a G protein-coupled receptor primarily known for regulating skin pigmentation and exhibiting anti-inflammatory effects. First, we provide an overview of the structure, signaling pathways, and related diseases of MC1R. Next, we discuss the potential therapeutic use of synthetic peptides and small molecule modulators of MC1R, highlighting the development of various drugs that enhance stability through amino acid sequence modifications and small molecule drugs to overcome limitations associated with peptide characteristics. Notably, MC1R-targeted drugs have applications beyond skin pigmentation-related diseases, which predominantly affect MC1R in melanocytes. These drugs can also be useful in treating inflammatory diseases with MC1R expression present in various cells. Our review underscores the potential of MC1R-targeted drugs to treat a wide range of diseases and encourages further research in this area.

The proper hydrogenation of Hyper-coal (HPC) using 1, 2, 3, 4-tetrahydroquinoline (THQ) was able to decrease the oxygen content and adjust the molecular structure of HPC for preparing the spinnable pitch with high softening point (SP). The spinnable pitch prepared from the THQ-soluble (QS) fraction of HPC as a precursor consisted more naphthenic carbon groups than that prepared from the 1-methylnaphthalene (1-MN) soluble (MNS) fraction of HPC. The HPC-QS derived pitch showed excellent spinnability even the SP of 260°C, and the tensile strength of the resultant carbon fiber was up to 1350 MPa with a diameter around 8 µm by only heat treatment at 800°C for 5 min.

Rice bran (RB) is a promising food ingredient that can improve biological function. In this study, we investigated the effects of RB, both unfermented (RB30) and fermented (RBF30) with five different microorganisms on the neurobehavioral activity in zebrafish larvae. Analytical methods such as UV spectrum and MS scan were performed. Interestingly, niacin content, which is known to improve brain functions such as cognition and emotion, was found to be higher in RBF30 than in RB30. Furthermore, niacin content was highly increased in the RBF30-exposed fish, compared to those in control fish. Therefore, we profiled behavioral patterns and various neurochemistry in zebrafish larvae following supplementation with RBF30 as well as performed calcium imaging on Tg (huC:GAL4-VP16);(UAS:GCaMP7a) zebrafish larvae to determine the correlation of neural activity. RBF30 revealed greater stimulation of locomotor activity without negatively affecting decision-making behavior in zebrafish larvae, as compared to RB30 or niacin. Its behavioral activation is mainly linked with the elevations of neural activity and several neurochemicals such as serotonergic and dopaminergic systems that are implicated in the control of anxiety and stress. Taken together, these results suggest that RBF30 could be a food material that improve the behavioral health by modulating neural activity and brain neurochemistry.

In this study, the microbial community during the barley yeast fermentation of stevia leaves and its correlation with antioxidant, antiobesity, and anti-inflammatory properties, as well as metabolites, were investigated using UPLC-Q-TOF-MS. Stevia was fermented using commercial TSB media (TSB 1, TSB 2, and TSB 3) and sucrose (Sucrose 1, Sucrose 3, and Sucrose 3) for 1, 3, and 5 days, respectively. Stevia ferments showed higher DPPH and ABTS radical scavenging capacity compared to samples incubated with sucrose for 5 days, and all six ferments inhibited nitric oxide production in a concentration-dependent manner in LPS-induced mouse macrophages. Furthermore, UPLC-QTOF-MS analysis identified 23 related substances, including 10 terpenoids, including rubusoside, steviolbioside, and rebadioside derivatives; dulcoside A; and phlomisoside II, which are indicators of stevia, as well as five flavonoids, four phenolic acids, and four fatty acids. We also identified the microbial community during fermentation via the next-generation sequencing of the 16S rRNA gene for bacteria and the internal transcribed spacer (ITS) gene for fungi. The results showed that TSB 1 and Sucrose 1 ferments were dominated by the pathogens Enterococcus hirae (58.93%) and Cronobacter sakazakii (80.92%), while samples fermented for more than 3 days were pathogen-free and dominated by lactic acid bacteria such as Pediococcus stilesii (73.37%). Microbial community analysis using the ITS region showed that Saccharomycopsis fibuligera, classified as a yeast rather than a mold, dominated the stevia fermentation regardless of the fermentation duration. In particular, the microbial community of the fermentation with a sugar solution was dominated by S. fibuligera by more than 99% throughout the fermentation periods of 1, 3, and 5 days. Finally, to apply the stevia ferments topically to human skin, skin irritation tests were performed on 30 volunteers. The results showed that the highly concentrated extracts (1 mg/mL) of all six stevia ferments were hypoallergenic. Taken together, these findings suggest that brewer's yeast fermented from stevia leaves with a sugar solution offers promise as a natural ingredient for use in functional foods and cosmetics.

Recently, various applications of indoor location-based services using Wi-Fi fingerprint data have been studied. However, since fingerprint data collected at a specific location does not have continuous information between signals and correlation information between signals, path data cannot exist in principle. Therefore, most indoor positioning technologies are based on predicting the position of a stationary positioner at a specific location based on signal data collected at a specific location in the indoor space. Due to the discontinuous nature of these signals, they are unable to account for user movement. Therefore, there is a need for techniques to improve the accuracy of indoor positioning in moving situations. This paper proposes a method for enriching all data points with relevant information to obtain improved indoor positioning results and an improved technique for generating movement path data. The proposed technique generates relevant information with continuity by expressing data points as Bounding Boxes (BB) and subsequently clustering them using grid cells. Through the proposed method, we represented and expanded the indoor location data, which consists of data points, as BB. However, data points represented by BB do not have any adjacency information with each other, and as a result, it is not possible to create a movement path between neighboring BB. To solve the problem, we used clustering, a machine learning technique that categorizes data points into groups, to group BBs. This was done by constructing a YOLO (You Only Look Once) grid cell that included all BBs and clustered them into grid cells that were divided into specific regions through the suggestion phase. The clustering results align with the indoor structure of the building, indicating that the data points have been appropriately clustered. BBs inside a grid cell are contained within a moveable area and have related information. We also generated association information between neighboring grid cells through the proposal technique, and then generated path information that can be traveled between grid cells. In this paper, we use the movement path information generated from a dataset as training data for machine learning, and through this, we propose an enhanced indoor positioning technology.

In this study, we propose a module-type triboelectric nanogenerator (TENG) capable of harvesting power from a variety of mechanical energy sources. The potential energy and kinetic energy of water are used for the rotational motion of the generator module, and electricity is generated by the contact/separation generation mode between the two triboelectric surfaces inside the rotating TENG. Through the parametric design of the internal friction surface structure and mass ball, we optimized the output of the proposed structure. To magnify the power, experiments were conducted to optimize the electrical output of the series of TENG units. The electrical signal generated by the module-type TENG can be used as a sensor to recognize the strength and direction of various physical quantities, such as wind or earthquake vibrations.

The synergistic efficacy of combined treatment mild heat (MH) and dielectric barrier discharge (DBD) plasma in Bacillus cereus-contaminated red pepper powder was tested. A cocktail of three strains of B. cereus (NCCP 10623, NCCP 14579, ATCC 11778) was inoculated onto red pepper powder and then treated with MH (60 ℃ for 5-20 min) and DBD plasma (5-20 min). Treatment with MH and DBD plasma alone for 5~20 min resulted in reductions of 0.23~1.43 and 0.12~0.96 log CFU/g, respectively. Combined treatment with MH and DBD plasma was the most effective at reducing B. cereus counts on red pepper powder and resulted in log-reductions of ≥ 6.0 log CFU/g. The largest synergistic values (4.24-4.42 log) against B. cereus in red pepper powder were obtained by the combination of 20 min MH and 5~15 min DBD plasma. Hunter color ‘‘L’’, ‘‘a’’, and ‘‘b’’ values of the combination-treated samples were not significantly different from those of non-treated samples. Also, no significant (p > 0.05) differences in pH values between samples were observed. Therefore, these results suggest that the combination of MH treatment and DBD plasma can be potentially utilized in the food industry to effectively inactivate B. cereus without incurring quality deterioration of red pepper powder.

Objective: The pleiotropic effect of hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins) is responsible for potent defense against inflammatory response. This study evaluated the inhibitory effects of HMG-CoA reductase inhibitors on monosodium urate (MSU)-induced inflammatory response through the regulation of interleukin-37 (IL-37) expression. Methods: Serum was collected from patients with gout (n = 40) and from healthy controls (n = 30). The mRNA and protein expression of target molecules IL-1b, IL-37, caspase-1, and Smad3 was measured in THP-1 cells stimulated with MSU, atorvastatin, or rosuvastatin using real-time quantitative polymerase chain reaction and Western blot assay. Transfection with IL-1b or Smad3 siRNA in THP-1 cells was used to verify the pharmaceutical effect of statins in uric acid-induced inflammation. Results: Serum IL-37 level in gout patients was significantly higher than in controls (p < 0.001) and was associated with serum uric acid level (r = 0.382, p = 0.008). THP-1 cells stimulated with MSU markedly induced IL-37 mRNA expression and transition of IL-37 from cytoplasm to nucleus. Recombinant IL-37 treatment dose-dependently inhibited activation of caspase-1 and IL-1b in MSU-induced inflammation. Atorvastatin and rosuvastatin attenuated caspase-1 activation and mature IL-1b expression but augmented translocation of IL-37 from cytoplasm to nucleus. Atorvastatin and rosuvastatin induced phosphorylation of Smad3 in THP-1 cells treated with MSU crystals. Statins potently attenuated translocation of IL-37 from cytoplasm to nucleus in THP-1 cells transfected with Smad3 siRNA compared to cells with negative control siRNA. Conclusion: This study revealed that statins inhibit MSU-induced inflammatory response through augmentation of phosphorylated Smad3-mediated IL-37 expression in THP-1 cells.

Background This study aimed to determine the association between immune checkpoint inhibitors (ICIs) and the risk of herpes zoster (HZ) incidence in patients with lung cancer. Method We obtained national claims data of 51,021 patients from South Korea with lung cancer between August 2017 and December 2021. The study population was classified into ICIs and non-ICIs groups based on prescription of ICIs at least once during the study period. To estimate the effects of ICIs treatment compared with those without ICIs treatment on HZ incidence, we used the Cox proportional hazards model adjusted for sex, age, comorbidities, and concomitant use of immunosuppressive drugs. Stratified analyses based on sex, age, and comorbidities were conducted to identify corresponding risk factors. Results Of the 51,021 study participants, 897 (1.8%) were prescribed ICIs, and 2,262 (4.4%) were diagnosed with HZ. Approximately 75.6% of the patients were male, and the prevalence of diabetes, cardiovascular disease, and chronic lung disease in the ICIs group was significantly lower than that in the non-ICIs group. The Kaplan–Meier plot showed that the probability of incidence of HZ in the ICIs group was lower than that in the non-ICIs group. Additionally, treatment with ICIs was associated with a 31% lower risk of developing HZ when compared to that seen without ICIs treatment (95% confidence interval [CI], 0.48–1.00). This association was stronger in females (hazard ratio [HR], 0.42; 95% CI, 0.19–0.94) and those less than 68 years of age (HR, 0.58; 95% CI, 0.34–0.99). Conclusion In these real-world data from an Asian population with lung cancer, ICIs treatment might be associated with a reduced risk of HZ compared to that without ICIs treatment.

Background and Objectives: The Temperament Evaluation of Memphis, Pisa, Paris and San Diego Autoquestionnaire (TEMPS-A) is designed to assess affective temperaments. The short version of TEMPS-A (TEMPS-A-SV) was translated into diverse languages for its broad application in research and clinical settings. However, no study was conducted to validate the Korean version of TEMPS-A-SV among patients with mood disorders. The purpose of this study is to examine the reliability and validity of the TEMPS-A-SV in mood disorder patients of the Korean population. Materials and Methods: In this cross-sectional retrospective study, a total of 715 patients (267 patients with major depressive disorder, 94 patients with bipolar disorder I, and 354 patients with bipolar disorder II) completed the Korean TEMPS-A-SV. Cronbach's alpha and McDonald’s omega was used to assess reliability. Exploratory factor analysis (EFA) was also performed. Spearman's correlation coefficient was used to examine associations between five temperaments. The difference in five temperament scores between gender or diagnosis groups were analyzed, and the correlation between five temperament scores and age were tested. Results: The Korean TEMPS-A-SV displayed good internal consistency (α = 0.65–0.88, ω = 0.66–0.9) and significant correlations between the subscales except one (correlation between hyperthymic and anxious). Using EFA, a two-factor structure was produced: Factor I (cyclothymic, depressive, irritable, and anxious) and Factor II (hyperthymic). The cyclothymic temperament score differed by gender and the anxious temperament score was significantly correlated with age. All the temperaments, except for irritable temperament, showed significant differences between diagnosis groups. Conclusions: Overall, our findings indicate that TEMPS-A-SV is a valid and reliable measure of estimating affective temperaments among Koreans. Our results confirm validity of TEMPS-A-SV among Korean patients with mood disorders. However, more study is required on affective temperaments and associated characteristics in people with mood disorders.

The horizontal flatbed electrophoresis system has been used for the electrophoresis of protein samples and is often preferred for a two-dimensional polyacrylamide gel electrophoresis. In currently available equipment, cathode and anode electrodes were placed on top of a gel at each end. An electric field entered from the top of a gel, and its strength was relatively weaker in the bottom part of the gel. Such configuration caused migrating protein bands to lie down forward in the migrating direction. New electrodes, i.e., the clamp-shaped and the double-deck electrodes, were developed to simultaneously apply an electric field from the top and bottom of a gel. Electrophoretic separation with these electrodes made protein bands more focused and improved protein band resolutions. When the FIGE technique was tested together with new electrodes, protein bands were aligned nearly vertically and showed the best electrophoretic resolutions.

The conserved MYST proteins form the largest family of histone acetyltransferases (HATs) that acetylate lysines within the N-terminal tails of histone, enabling active gene transcription. Here, we have investigated the biological and regulatory functions of the MYST family HAT Sas3 in the opportunistic human pathogenic fungus Aspergillus fumigatus using a series of genetic, biochemical, pathogenic, and transcriptomic analyses. The deletion of sas3 results in drastically reduced colony growth, asexual development, spore germination, response to stresses, and the fungal virulence. Genome-wide expression analyses have revealed that the sas3 mutant showed 2,402 significant differentially expressed genes; 1,147 up-regulated and 1,255 down-regulated. The representative up-regulated gene resulting from sas3 is hacA predicted to encode a bZIP transcription factor, whereas the UV-endonuclease UVE-1 was significantly down-regulated by sas3. Furthermore, our Western blot analyses suggest that Sas3 likely catalyzes acetylation of H3K9, K3K14, and H3K29 in A. fumigatus. In conclusion, Sas3 is associated with diverse biological processes and can be a potential target for controlling pathogenic fungi.

A two-year-old male Pomeranian dog visited the hospital due to side effects of surgical correction for patellar luxation. Stifle joint arthrodesis (SJA) was performed on the patient’s right leg using autologous bone grafting techniques. The right leg’s femur and tibial joint were angled 120-130◦ and the SJA plate was fixed on the front of the two bones. After performing joint fusion of the right limb, medial patellar luxation (MPL)-corrective surgery was performed to cut the tibial tuberosity on the left leg and the fixing force was increased using the figure-of-eight tension band wiring technique. The results were observed for walking and trotting of the right hind limb, evaluated for 27 days after surgery. It was difficult for the patient to walk because weight loading was not performed for 3 days after surgery; short strides and partial weight bearing were possible 5 to 7 days after surgery. After 10 days, the patient was able to move while bearing weight with a slight disruption. For trotting, the patient showed intermittent normal steps 5 to 7 days after surgery, but the disruption continued. After 14 days, trotting was possible and movements were shown to be maintained during everyday activities.

The Korean water deer (WD), the dominant wildlife species in South Korea, is classified as vul-nerable on the IUCN Red List. Beside their several unique characteristics, they have a lower in-cidence of tick bites compared to other Cervidae species, particularly roe deer (RD). In natural environments, ticks exhibit questing behavior by waiting on grasses to climb onto host's hair coat. They use sensory organs to detect signals such as body temperature and chemicals from the host's skin, then move to preferred feeding location. Therefore, we conducted a comparative analysis of the skin tissue and hair features of both species to compare the factors leading to lower incidence of tick bites in WD. Notably, WD had significantly larger blood vessels, sebaceous glands, and sweat glands which maintain the barrier functions of the skin (p≤0.004), irregular scale surfaces in hair cuticles, and stiff and 2.83-times thicker primary hair diameter (p<0.001). Each parameters made the ticks difficult to move onto the epidermis of WD. To our knowledge, this is the first report which is focused on the comparative analysis for skin and hair feature in WD and RD. We suggest that these differences may attribute to evade tick bites in WD.

Recently, two respiratory syncytial virus (RSV) vaccines based on the prefusion F (pre-F) antigen were approved in the United States. We developed and validated an enzyme-linked immunosorbent assay (ELISA)-based protocol for the practical and large-scale evaluation of RSV vaccines. Two modified pre-F proteins (DS-Cav1 and SC-TM) were produced by genetic recombination and replication using an adenoviral vector. The protocol was established by optimizing the concentrations of the coating antigen (pre-F proteins), secondary antibodies, and choice of blocking buffer. To validate the protocol, we examined its accuracy, precision, and specificity using serum samples from 150 participants across various age groups and the standard serum provided by the National Institute of Health. In the linear correlation analysis, coating concentrations of 5 and 2.5 μg/mL of DS-Cav1 and SC-TM, respectively, showed high coefficients of determination (r2 = 0.90). Concentrations of secondary antibodies (alkaline phosphatase-conjugated anti-human immunoglobulin G, diluted 1:2000) and blocking reagents (5% skim milk/PBS-T) were optimized to minimize non-specific reactions. High accuracy was observed for DS-Cav1 (r2 = 0.90) and SC-TM (r2 = 0.86). Further, both antigens showed high precision (coefficient of variation &lt; 15%). Inhibition ELISA revealed cross-reactivity of antibodies against DS-Cav1 and SC-TM but not with the attachment protein.

This study delves into the critical domain of public procurement, specifically focusing on Sustainable Public Procurement (SPP) and Environmental, Social, and Governance (ESG) considerations within the context of South Korea. The aim of this research is to empirically scrutinize the factors influencing the impact index of SPP, with a particular emphasis on services related to labor standards. The investigation spans three years, from January 1, 2019, to July 31, 2021, encompassing state agency orders and post-implementation legal changes. Leveraging the comprehensive Korea On-line E-Procurement Sys-tem(KONEPS) platform, we meticulously collected data, ultimately culminating in 21,573 services meeting our criteria, from which 250 cases underwent an in-depth analysis. The study sheds light on the influence of social responsibility evaluation cri-teria on overall procurement scores, while also uncovering the moderating effects of various factors. The outcomes of this study offer valuable insights into the impact index of social responsibility evaluation criteria within South Korea's public procurement landscape. Factors including procurement year, classification, service period, and estimated price were identified as significant influencers. Moreover, the number and location of evalu-ation criteria items, inclusion of sincerity elements, and the evaluating agency were deemed pivotal determinants of the influence index. These findings hold profound implications for policymakers, procurement practitioners, and researchers and provide a roadmap for optimizing the utilization of social responsibility evaluation criteria to advance responsible and sustainable procurement practices. Future research avenues include conducting further study of additional moderat-ing variables, expanding datasets to encompass a broader spectrum of procurement contexts, and evaluating the long-term repercussions of social responsibility evaluation on procurement outcomes. These endeavors promise to further refine and advance public procurement practices in South Korea and beyond.

Gastric cancer, also known as stomach cancer, is a cancer which develops from the lining of the stomach. Accumulated evidences and epidemiological studies have been indicated that natural products play an important role in gastric cancer prevention and treatment, although its mechanism of action did not elucidate yet. Particularly, experimental studies have been showed that natural products displayed a protective effect against gastric cancer via numerous molecular mechanisms such as suppression of cell metastasis, anti-angiogenesis, inhibition of cell proliferation, induction of apoptosis, and modulation of autophagy. Although chemotherapy remains the standard treatment for advanced gastric cancer along with surgery, radiation therapy, hormone therapy and immunotherapy, but its adverse side effects including neutropenia, stomatitis, mucositis, diarrhea, nausea, and emesis are well documented. Additionally, intake of naturally occurring phytochemicals could increase the efficacy of gastric chemotherapy and chemotherapeutics resistance. However, natural product structural stability and powerful bioactivity are important to develop novel treatments for gastric cancer that may minimize such adverse effects. Therefore, the purpose of this review is to summarize the potential therapeutic effects of natural products on prevention and treatment of gastric cancer with intensive molecular mechanisms of action, bioavailability, and safety efficacy.

Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) are comorbidities that result from the sharing of common genes. The molecular background of comorbidities can provide clues for the development of treatment and management strategies. Here, the common genes involved in the development of the two diseases and in memory and cognitive function are reviewed. Network clustering based on protein-protein interaction networks identified tightly connected gene clusters that have an impact on memory and cognition among the comorbidity genes of AD and T2DM. Genes with functional implications were intensively reviewed, and relevant evidence was summarized. Gene information will be useful in the discovery of biomarkers and the identification of therapeutic targets for AD.

The prediction of corticosteroid responses in coronavirus disease 2019 (COVID-19) patients is crucial in clinical practice, and exploring the role of artificial intelligence (AI)-assisted analysis of chest radiographs (CXR) is warranted. This retrospective case-control study involving hospitalized COVID-19 patients treated with corticosteroids was conducted from September 4th, 2021, to August 30th, 2022. The primary endpoint of the study was corticosteroid responsiveness, defined as the advancement of two or more of the eight-categories-ordinal scale. Serial abnormality scores for consolidation and pleural effusion on CXR were obtained using a commercial AI-based software based on days from onset of symptoms. Amongst the 258 participants included in the analysis, 147 (57%) were male. Multivariable logistic regression analysis revealed that high pleural effusion score at 6–9 days from onset of symptoms (adjusted odds ratio of [aOR]: 1.022, 95% confidence interval [CI]: 1.003-1.042, p=0.020) and consolidation scores up to 9 days from onset of symptoms (0-2 days: aOR: 1.025, 95% CI: 1.006-1.045, p=0.010; 3-5 days: aOR: 1.03 95% CI: 1.011-1.051, p=0.002; 6-9 days: aOR; 1.052, 95% CI: 1.015-1.089, p=0.005) were associated with an unfavorable corticosteroid response. AI-generated scores could help intervene in the use of corticosteroids in COVID-19 patients who would not benefit from them

In this paper, we present a study of various ZnO/SiO2-stacked thin film structures for flexible micro-energy harvesting devices. Two groups of micro-energy harvesting devices, SiO2/ZnO/SiO2 micro-energy generators (SZS-MGs) and ZnO/SiO2/ZnO micro-energy generators (ZSZ-MGs), were fabricated by stacking both SiO2 and ZnO thin films, and the resulting devices were characterized. With a particular interest in the fabrication of flexible devices, all the ZnO and SiO2 thin films were deposited on ITO-coated PEN substrates using an RF magnetron sputtering technique. The effects of the thickness and/or position of the SiO2 films on the device performance were investigated by observing the variations of output voltage in comparison with that of a control sample. As a result, compared to the ZnO single-layer device, all the ZSZ-MGs showed much better output voltages, while all the SZS-MG showed only slightly better output voltages. Among the ZSZ-MGs, the highest output voltages were obtained from the ZSZ-MGs where the SiO2 thin films were deposited using a deposition power of 150 W. Overall, the device performance seems to depend significantly on the position as well as the thickness of the SiO2 thin films in the ZnO/ SiO2-stacked multilayer structures, in addition to the processing conditions.

We still do not have satisfactory answers about the inflationary and accelerating expansions of the universe, the missing mass problem, and the origins of the four fundamental forces and the quantum phenomena. Here, we introduce a new model of matter and space and a postulate on a constant of motion, and then show that an equation of motion derived from the constant of motion could provide new understandings of the above problems. For example, we show that there is a natural inflation scenario and that the co-moving distance to the closest accelerating supernova is clearly given as $c/(\sqrt{3} H)$ ($\sim$8.1 billion ly). In addition, we show that, in a system including a proton and an electron, the equation of motion has eight acceleration terms, some of which are suspected to be responsible for the four fundamental forces and the galaxy rotation anomaly. From further discussion, we conjecture that in order to understand the gravitational and strong interactions and the quantum phenomena, it is necessary to consider the expansion of the universe. The lensing mass distribution is explained, based on another postulate on a method of representing superposition.

In this paper, we have proposed an effective metasurface design to accomplish cloaking of equilateral patch antennas and their array configuration. As such, we have exploited the concept of electromagnetic invisibility, employing the mantle cloaking technique with the intention to eliminate the destructive interference ensuing between two distinct triangular patches situated in a very congested arrangement (sub-wavelength separation is maintained between the patch elements). Basically, we demonstrate that implementation of the planar coated metasurface cloaks onto the patch antenna surfaces compels them to become invisible to each other, at the intended frequencies. In effect, an individual antenna element does not sense the presence of the other, in spite of being in a rather close vicinity. We also exhibit that the cloaks successfully reinstate the radiation attributes of each antenna in such a way that it emulates their respective performance in an isolated environment. Moreover, we have extended the cloak design to an interleaved one dimensional array of the two patch antennas and it is shown that the coated metasurfaces assure efficient performance of each array in terms of their matching as well as radiation characteristics, in turn, enabling them to radiate independently for various beam-scanning angles.

Low energy consumption is a significant contribution of new and retrofitted buildings that place an emphasis on energy efficiency. Although the mandatory requirements of the U-factor and R-value are described in the Vietnamese National Technical Regulation on the energy efficiency of buildings—QCVN09:2017/BXD, these values are not widely publicized, leading to designers facing difficulties when choosing suitable materials at the design stage. This study focuses on the method used to declare the heat transfer values of building materials based on the energy labeling process in order to create an energy labeling program for building materials. To accomplish this objective, criteria and labeling procedures for windows, doors, walls, and building roofs have been developed. In addition, a case study of insulation product labeling was used to evaluate the applicability of the developed labeling program in the implementation phase.

Resection of malignant lesions in the oral and maxillofacial area causes functional and morphological defects. To recover from these defects, reconstruction surgery is needed such as a primary closure or a flap. There are advantages and disadvantages to the method of using the primary closure and the flap, and the choice of procedure is entirely up to a surgeon. The purpose of this study is to evaluate availability of primary closure in patients. For 10 years from January 2010 to May 2020, patients who recovered using flaps after removing malignant lesions from the oral cavity and those who closed the lesion using primary closure were investigated. The investigation was conducted by searching the database of the hospital. There were 85 patients who removed malignant lesions in the oral and maxillofacial area, and among them 16 patients closed the soft tissue defects using primary closure. Operation time, histologic types, stages, wound status, and period of hospitalization according to the patients' medical records were investigated retrospectively. Between the staging and the primary closure, statistically significant differences were observed between stage I and stage II patients in 13 patients using the primary closure. (P=0.046). Statistically significant differences were identified between operation time and primary closure, with an average of 4.02 hours (P=0.015). The average period of hospitality for patients who underwent primary closure was 13.4 days, compared to 26.7 days for those who underwent flap surgery. This difference was statistically significant (P=0.0003). This study evaluated the use of primary closure in patients who underwent resection of malignant lesions in the oral and maxillofacial area. Although the majority of patients underwent reconstruction with flaps, our results showed that primary closure can be a viable option for certain patients with early-stage lesions. Therefore, oral and maxillofacial surgeons should carefully consider various evaluation criteria and select appropriate reconstruction methods to optimize functional and aesthetic outcomes for each patient.

Using the World Bank Global Findex Database for 91 countries in 2014, 2017, and 2021, we examine whether fintech levels influence bank performance and whether fintech’s interaction with GDP per capita causes differential effects on bank performance globally. Since fintech levels were already very high for rich countries when the World Bank started providing fintech development statistics in 2014, we estimate AbFintech by regressing fintech levels on GDP per capita by year. AbFintech is the difference between the fintech level and its fitted values. Then, using multiple regression analyses, we investigate the impact of AbFintech on bank performance worldwide, focusing on the differential effects of AbFintech and GDP levels on bank performance. We find AbFintech significantly increases bank performance, primarily in less developed countries. Specifically, AbFintech increases banks’ ROA in the least developed countries and net interest margin in the 75th percentile countries. Also, AbFintech decreases the cost-to-income ratio in the 75th percentile countries, while it increases the ratio in the most developed countries. AbFintech does not affect the ratio of noninterest income to total income, regardless of the level of economic development.

Changes in extreme weather patterns are expected under climate change. In this study, a risk assessment was conducted using 4 building damage history datasets and 33 weather datasets (precipitation, wind speed, snow, and temperature) from 230 regions in South Korea to quantitatively analyze and predict building damage caused by potential future natural disasters. Decision tree analysis was used to evaluate building damage risk in 230 regions. The decision tree model to determine the risk of flood, gale, and typhoon was generated, which excluded gales, with less damage. The weight (variable importance) and limit value (damage limit) of the weather variables ware derived using the decision tree model. Using these two factors, we assessed the building damage risk in 230 regions in South Korea until 2100. The number of regions at risk of flood damage increased by more than 30% in average. Conversely, regions at risk of snowfall damage decreased by more than 90%. The regions at risk of typhoons decreased by 57.5% on average, and the number of regions at high risk of typhoon damage increased by up to 62.5% in RCP 8.5. These results can be used as objective data to minimize future building damage throughout South Korea, representing the first step towards sustainable development in the region with respect to disaster response.

Electromagnetic cloaking approach is employed with the intention to curb the destructive effects of mutual interference for rectangular and circularly shaped patch antennas situated in a tight spacing (sub-wavelength separation is employed). Primarily, we show that by coating the top surface of each patch with an appropriately designed metasurface, it is possible to considerably reduce the mutual coupling between the said antennas. We also demonstrate that, despite the close proximity of these patches, the individual radiation characteristic of each antenna is reinstated to emulate their performance in an isolated scenario. Furthermore, the cloak construct is extended to a tightly spaced, interleaved linear patch antenna array configuration and it is shown that the coated metasurfaces successfully enhance the performance of each array in terms of their matching characteristics, total efficiencies as well as their far-field realized gain patterns for a broad range of beam-scan angles. The decoupling and cloaking action of these metasurfaces are demonstrated by various full-wave numerical simulation results. We believe that the simple planar structure of our metasurface design (according to simulation models) makes it feasible for fabrication and the fact that it is used to accomplish cloaking of an electrically large antenna surface, makes it unique in itself.

This study develops an intelligent fire suppression device that links microcapsule-based IoT to extinguish early fires, such as blind spots that cannot be seen by humans or places that are difficult to recognize fires. The microcapsule is a micro-unit collection that collects Novak 1230 gas generated in the semiconductor production process. The microcapsule is molded into a form with a fire suppression function, and when a fire occurs, the molded body explodes and absorbs ambient oxygen to suppress the fire. The complex sensor IoT executes smoke and heat detection generated when a fire is suppressed within 10 seconds, which derives the reliability of the detector by notifying the fire and detecting the ignition point through communication linkages such as Ieee 485 and WiFi or LoRa.

Vein blood sampling is one of the methods of mass blood sampling, and is an act of drawing blood from a vein for blood type discrimination, confirmation of various physiological indicators, disease diagnosis, and the like, and is the most commonly used blood sampling method. An important point in vein blood sampling is to search for the exact location of the vein and insert the blood sampling. This is influenced by the patient's obesity, skin and blood vessel conditions, and the experience of the clinical technologist, nurse, and resident who performs the blood sampling. It is required to perform blood sampling technique practice. However, due to many limitations of the practice room or laboratory, there is a problem of using only a limited environment and model for clinical practice. As a result, many medical educational institutions have situations in which only fragmentary clinical practices are performed, and it is difficult to per-form a large number of blood sampling skills practices, so they do not provide enough experience to understand the actual skill field. In this paper, we propose a method for developing a virtual reality-based vein blood sampling simulator to practice a large number of blood sampling techniques without restrictions. The proposed vein blood sampling simulator can operate a 3D model related to vein blood sampling using HMD Controller and Haptic in a virtual space for vein blood sampling practice by wearing an HMD (Head mounted display), and can perform vein blood sampling practice through interaction with the patient 3D model. there is. In addition, the effectiveness of the simulator developed for dental students was verified, and as a result of the verification, the potential of the proposed vein blood sampling simulator was confirmed.

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a promising chemotherapeutic agent because of its selective apoptotic action on cancer cells. However, resistance to TRAIL-induced apoptosis remains a challenge in many cancers. The gintonin-enriched Panax ginseng extract fraction (GEF) has diverse pharmacological benefits. We explored the combined efficacy of GEF and TRAIL in inducing apoptosis in human renal cell carcinoma (RCC) cells. The effect of GEF treatment on viability, clonogenic potential, wound healing, and TRAIL-induced apoptotic signaling of RCC cells was studied in vitro. Our investigation revealed that GEF pre-treatment sensitized RCC cells to TRAIL-induced apoptosis, as evidenced by DNA fragmentation and cell proliferation, colony formation, and migration inhibition. This sensitization was linked to the upregulation of death receptors 4 and 5 and alterations in apoptotic protein expression; notably, decreased expression of Mu-2-related death-inducing gene, a novel anti-apoptotic protein. Our findings underscore the necessity of caspase activation for GEF/TRAIL-induced apoptosis using the pan-caspase inhibitor Z-VAD-FMK. It demonstrates that GEF sensitizes human RCC cells to TRAIL-induced apoptosis by upregulating DR4/5 and modulating apoptotic protein expression. These findings suggest a promising strategy for overcoming TRAIL resistance in cancer therapy and highlight the potential of GEF as a valuable adjunct to TRAIL-based treatments.

Since electrical resistivity of concrete can be measured in a more rapid and simple way than chloride diffusivity, it should be primarily regular quality control of the electrical resistivity of concrete which provides the basis for indirect of quality control of concrete durability during the concrete construction. If this is realized, the electrical resistivity of concrete can be a crucial parameter to establish maintenance strategy for marine concrete structures. Electrical resistivity of concrete is important to estimate two processes involved in corrosion of reinforcement: initiation (chloride penetration) and propagation (corrosion rate). The resistivity of concrete structure exposed to chloride indicates the risk of early corrosion damage, because a low resistivity is related to rapid chloride penetration and to high corrosion rate. Concrete resistivity is a geometry-independent material property that describes the electrical resistance, which is the ratio between applied voltage and resulting current in a unit cell. The current is carried by ions dissolved in the pore liquid. While some data exist on the relationship between moisture content on electrical resistivity of concrete, very little research has been conducted to evaluate the effect of chloride on the conduction of electricity through concrete. The purpose of this study is to examine and quantify the effect of pore water and chloride content on surface electrical resistivity measurement of concrete. It was obvious that chloride content had influenced the resistivity of concrete and the relationship showed a linear function. That is, concrete with chloride ions had a comparatively lower resistivity. Chloride can lead to underestimate the electrical resistivity of concrete. Conclusively, this paper suggested the quantitative solution to depict the electrical resistivity of concrete with various chloride contents.

Bayesian nonlinear mixed effects models for data in the form of continuous, repeated measurements from a population, also known as Bayesian hierarchical nonlinear models, are a popular platform for analysis when interest focuses on individual specific characteristics and relevant uncertainty quantification. Due to the limitation of computational power, this framework was relatively dormant until the late 1980s, but in recent years, the statistical research community saw vigorous development of new methodological and computational techniques for these models, the emergence of software, and wide application of the models in numerous industrial and academic fields. This article presents an overview of the formulation, interpretation, and implementation of Bayesian nonlinear mixed effects models and surveys recent advances and applications.

Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by current machine learning techniques because of a lack of its physiological understanding. To investigate the suitability of FC in BCI for the elderly, we propose the decoding of lower- and higher-order FCs using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. Seventeen younger (24.5±2.7 years) and twelve older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increases the classification accuracy by 88.3% compared to the filter-bank common spatial pattern (FBCSP). LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe depending on task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

This study aims to evaluate the climate-change response of greening persistently vacant houses and barren sites in deteriorated high-density residential areas in old downtown metropolitan areas. The current status of building demolition sites in Ami-dong and Chojang-dong, Busan Metropolitan City, was examined. In this area, of the 340 vacant houses from 2017, 246 (72.35%) remained persistently vacant till 2020. In 2020, 213 barren sites were neglected after building demolition. Amongst these sites, 177 (83.10%) were paved with concrete. Persistently vacant houses and barren sites were afforested, and the climate-change response was quantified in terms of flood reduction and CO2 mitigation in four scenarios (current, concrete, ground cover plants, and trees). Runoff analysis was performed using the Korea low-impact development model (K-LIDM) to assess flood mitigation. The tree scenario showed average runoff mitigations of 1.71%, 2.38%, and 3.06% in the long-term, 30-year, and 2-year rainfall scenarios, respectively. Additionally, 3058 deciduous broadleaf trees may be planted in the study area to promote CO2 mitigation. Thus, an additional annual carbon fixation of 62,123.78 kg CO2 may be expected. The greening of vacant houses and demolished sites was effective in all aspects of the climate-change response, and tree-based greening exhibited the most significant effect.

Cerebral ischemic stroke is one of the leading causes of death and disability worldwide. 2’-fucosyllactose (2’-FL), a human milk oligosaccharide, has anti-inflammatory effects and plays a protective role in arterial thrombosis, but its role in ischemic stroke remains unclear. This study aimed to investigate the neuroprotective effects of 2’-FL and its potential mechanisms in a mouse model of ischemic stroke. The administration of 2’-FL led to a reduction in the size of cerebral infarct and reactive oxygen species (ROS)-related products in the brain of MCAO mice. Further, 2’-FL promotes the recovery of neurological deficits and motor function in neurological scores and behavior tests. Biochemical studies showed that 2’-FL reduced upregulated IL-10 and downregu-lated TNF-α. Additionally,2’-FL enhanced M2-type microglial polar2’-FL upregulated IL-10 and downregulated TNF-α ization at 7 days after MCAO, with an increase in IL-4 levels and STAT6 activation at 3 days after MCAO. Our data showed that 2’-FL reduced the neurological symptoms of ischemic stroke and ROS accumulation in the brain through IL-4/STAT6-dependent M2-type mi-croglial polarization in MCAO mice. These results demonstrate that 2’-FL is a potentially effective therapeutic agent for ischemic stroke.

The inter-subspecific crossing between indica and japonica subspecies in rice have been utilized to improve the yield potential of temperate rice. In this study, a comparative study of the genomic regions in the eight high-yielding varieties (HYVs) was conducted with those of the four non-HYVs. The Next-Generation Sequencing (NGS) mapping on the Nipponbare reference genome identified a total of 14 common genomic regions of japonica-originated alleles. Interestingly, the HYVs shared japonica-originated genomic regions on nine chromosomes, although they were developed through different breeding programs. A panel of 94 varieties was classified into four varietal groups with 38 single nucleotide polymorphism (SNP) markers from 38 genes residing in the japonica-originated genomic regions and 16 additional trait-specific SNPs. As expected, the japonica-originated genomic regions were only present in the japonica (JAP) and HYV groups, except for Chr4-1 and Chr4-2. The Wx gene, located within Chr6-1, was present in the HYV and JAP variety groups, while the yield-related genes were conserved as indica alleles in HYVs. The japonica-originated genomic regions and alleles shared by HYVs can be employed in molecular breeding programs to further develop the HYVs in temperate rice.

Manipulating autophagy is a promising strategy for treating cancer as several autophagy inhibitors shown to induce autophagic cell death. One of these, autophagonizer (APZ), induces apoptosis-independent cell death by binding an unknown target via an unknown mechanism. To identify APZ targets we used a label-free drug affinity responsive target stability (DARTS) approach with a liquid chromatography/tandem mass spectrometry (LC-MS/MS) readout. Of 35 protein interactors, we identified Hsp70 as a key target protein of unmodified APZ in autophagy. Either APZ treatment or Hsp70 inhibition attenuates integrity of lysosomes, which leads to autophagic cell death exhibiting an excellent synergism with a clinical drug, temozolomide, in vitro, in vivo, and orthotropic glioma xenograft model. These findings demonstrate the potential of APZ to induce autophagic cell death and its development to combinational chemotherapeutic agent for glioma treatment. Collectively, our study demonstrated that APZ, a new autophagy inhibitor, can be used as a potent antitumor drug candidate to get over unassailable glioma and revealed a novel function of Hsp70 in lysosomal integrity regulation of autophagy.

In this study, we evaluated the antibacterial effect against Staphylococcus aureus and Salmonella Typhimurium in fried fish paste using floating electrode-dielectric barrier discharge (FE-DBD) plasma (1.1 kV, 43 kHz, N2 1.5 m/s, 1-60 min). In addition, quality evaluation (pH, VBN) of fried fish paste was conducted after FE-DBD plasma treatment. When FE-DBD plasma was treated for 1, 5, 10, 20, 30 and 60 min, S. aureus decreased by 0.16-1.13 log10 CFU/g, and S. Typhimurium decreased 0.25-1.13 log10 CFU/g. Both decreased &gt; log10 CFU/g at 60 min. The D-value was 58.92 and R2 was 0.97 for S. aureus using first-order kinetics, and the D-value was 43.60 and R2 was 0.97 for S. Typhimurium using the Weibull model. There was no significant difference in pH after FE-DBD plasma treatment (p &gt; 0.05). However, volatile basic nitrogen (VBN) significantly decreased as the treatment time increased (p &lt; 0.05), and it was the lowest at 60 min to 3.46. Therefore, FE-DBD plasma treatment could be considered as a technology for preserving the quality of processed foods.

This study was conducted to investigate the prevalence of Coxiella burnetii infection according to cattle breeds and growth types. A total of 491 cattle [cattle breed: 216 dairy cattle and 275 beef cattle; according to growth type: indoor housing (n = 294) and grazing (n = 197)] were tested for the presence of C. burnetii DNA and antibodies against C. burnetii using a commercial enzyme-linked immunosorbent assay (ELISA). Overall, 10.8% and 8.8% of the cattle were positive by PCR and for C. burnetii antibodies, respectively. The prevalence of C. burnetii was significantly higher in beef cattle than in dairy cattle using PCR (13.6% vs 7.4%; P = 0.032) and ELISA (14.6% vs 1.4%; P = 0.000), respectively. The overall infection rate of C. burnetii was significantly high in grazing cattle (PCR: 24.9%, ELSIA: 21.3%; P = 0.000) compared with housing cattle (PCR: 1.4%, ELISA: 0.3%). The results indicate that beef cattle have a significantly higher risk of contracting C. burnetii infection compared with dairy cattle (21.5% vs. 7.9%, χ2 = 5.82, P = 0.000, odds ratio = 3.197, 95% CI: 1.80-5.67). In addition, the infection of C. burnetii was significantly associated with grazing (P = 0.000). Moreover, a risk of contracting C. burnetii infection in grazing cattle was increased by 32.57-fold (95% CI: 12.84-82.60, P = 0.000) compared with indoor housed cattle. The phylogenetic analysis based on the IS111 gene revealed that our isolates were grouped together with humans, ticks, goats, and cattle isolates found in several countries. C. burnetii isolates circulating in the Republic of Korea exhibit genetic variations. Consequently, our results suggest that cattle are potential reservoirs for C. burnetii infection and most importantly, grazing acts as a high risk factor for the occurrence and transmission of this infection.

Management of crowding at subway platform is essential to improving services, preventing train delays and ensuring passenger safety. Establishing effective measures to mitigate crowding at platform requires accurate estimation of actual crowding levels. At present, there are temporal and spatial constraints since subway platform crowding is assessed only at certain locations, done every 1~2 years, and counting is performed manually Notwithstanding, data from smart cards is considered real-time big data that is generated 24 hours a day and thus, deemed appropriate basic data for estimating crowding. This study proposes the use of smart card data in creating a model that dynamically estimates crowding. It first defines crowding as demand, which can be translated into passengers dynamically moving along a subway network. In line with this, our model also identifies the travel trajectory of individual passengers, and is able to calculate passenger flow, which concentrates and disperses at the platform, every minute. Lastly, the level of platform crowding is estimated in a way that considers the effective waiting area of each platform structure.

Although numerous citrus varieties have recently been developed to enhance their quality, information on their quality characteristics is limited. We assessed the quality characteristics of Yellowball, a novel citrus variety, by evaluating its appearance, storability, sensory properties, functionality, and metabolite profiles and comparing these characteristics with those of its parent varieties: Haruka and Kiyomi. The metabolite profiles between the citrus varieties differed significantly, resulting in distinct physicochemical and functional qualities. The storability of Yellowball was significantly increased compared with that of its parent varieties owing to its strong antifungal activity and unique peel morphology, including the stoma and albedo layers. While we did not investigate the volatile compounds, overall functional activities, and detailed characteristics of each metabolite, our data provide valuable insights into the relationship between citrus metabolites, peel morphology, physicochemical properties, and storability and demonstrate the potential of Yellowball as a promising variety in the citrus industry.

In this paper, a positive answer to the open question of Fabino et al. is given, and the concept of I\c sik type set valued contractions is introduced and fixed point theorem for such contractions is established. Examples to support main theorems are given, and an application to integral inclusion is given.

As a procedure deriving UH (unit hydrograph), the root selection method necessitates only storm runoff data. However, this method must deal with the uncertainty related to the noise fluctuation of runoff ordinates and derive one optimal UH from many storms. This study proposes a procedure that applies the Savitzky-Golay filter to smooth the noise fluctuation of the runoff ordinates and uses the linear combination of UHs from individual storms to derive an optimal UH. The proposed method is applied to the storms of the Nenagh River basin in Ireland. The applicability of the Savitzky-Golay filter for smoothing the noise fluctuation of storm runoffs is examined by means of the Nash-Sutcliffe efficiency index. Furthermore, the root selection method is extended to also estimate IUHs. The results show that the adoption of the Savitzky-Golay filter improves the applicability of the root selection method and that the optimal UH predicts accurately the time-to-peak and peak discharge.

This contribution addresses dry sliding friction of a “Non-Asbestos Organic” friction material (pad) rubbed against a pearlitic grey cast iron disc. This configuration is commonly used in brakes in US, Europe and Asia. The initial stage of friction is of particular interest when addressing “creep-groan” phenomena occurring in passenger and sport utility vehicles with automatic transmissions. The custom built Universal Friction Tester allowing the variation of sliding speed, test temperature, normal load, stiffness and humidity of the system was used in this experiment in combination with surface analysis by polarized light microscopy, scanning electron microscopy and stylus profilometry. The obtained results indicate that the “stick” phase in the so-called “stick-slip” phenomenon does not really exist. The surfaces of pad and disc are in continuous relative movement. The complexity of surfaces does not allow for a definitive description of exact mechanisms responsible for the detected changes of friction forces. The observed friction process can be rather described as a stretching with possible localized relaxations causing the jerky behavior in the stretching phase, followed by slip between two materials in contact. Understanding of factors contributing to the stretching at different scale levels of friction system is necessary for development of proper models. An accurate friction model should also incorporate the vibrational element introduced by phenomena occurring at the friction surfaces. The absence of heterogeneous regions on the friction surface and “evenly distributed friction level” can help when mitigating creep-groan in the investigated brake system. Understanding of factors on different scales of friction is also required when developing the improved brakes.

Corresponding to the principles of biological synapses, an essential prerequisite for hardware neural networks using electronics devices is continuous regulation of conductance. We implemented artificial synaptic characteristics in a (GeTe/Sb2Te3)16 iPCM with a superlattice structure under optimized identical pulse trains. Based on atomically controlling the Ge switch in the phase transition that appears in the GeTe/Sb2Te3 superlattice structure, multiple conductance states were implemented by applying the appropriate electrical pulses. Furthermore, we found that the bidirectional switching behavior of a (GeTe/Sb2Te3)16 iPCM can achieve a desired resistance level using the pulse width. Therefore, we also fabricated a Ge2Sb2Te5 PCM and designed a pulse scheme based on the phase transition mechanism to compare to the (GeTe/Sb2Te3)16 iPCM. We designed an identical pulse scheme that implements linear and symmetrical LTP and LTD based on the iPCM mechanism. As a result, the (GeTe/Sb2Te3)16 iPCM showed relatively excellent synaptic characteristics by implementing gradual conductance modulation, a nonlinearity value of 0.32, and LTP/LTD 40 conductance states using identical pulses trains. Our results demonstrate the general applicability of the artificial synaptic device for potential use in neuro-inspired computing and next generation non-volatile memory.

Long-haul COVID-19 illness first gained widespread recognition among social support groups and later in scientific and medical communities. This illness is mysterious as it affects COVID-19 survivors at all levels of disease severity, even younger adults and children. While the precise definition may be lacking, the defining symptoms are fatigue, dyspnea, and headache that last for months after hospital discharge. The less typical symptoms may include cognitive impairments, chest and joint pains, myalgia, smell and taste dysfunctions, cough, mood changes, and gastrointestinal and cardiac issues. Presently, there is limited literature discussing the possible pathophysiology, risk factors, and treatments in long-haul COVID-19, which the current review aims to address. In brief, long-haul COVID-19 may be driven by long-term lung and brain damage and unresolved inflammation from multiple sources. The associated risk factors may include female sex, more than five early symptoms, early dyspnea, and specific biomarkers like D-dimer. While only rehabilitation training has been useful for long-haul COVID-19, therapeutics repurposed from mast cell activation syndrome, myalgic encephalomyelitis/chronic fatigue syndrome, and pulmonary fibrosis also hold potential. In sum, this review hopes to provide the current understanding of what is known about long-haul COVID-19.

This narrative review provides a thorough examination of medication-related osteonecrosis of the jaw (MRONJ), encompassing its etiology, the role of drug holidays, and contemporary treatment approaches. By systematically analyzing literature from 2022 to 2023, this paper synthesizes the latest findings and trends in MRONJ research. The study emphasizes the importance of a holistic understanding of MRONJ, beginning with its etiological factors, assessing the therapeutic implications of drug holidays, and evaluating the efficacy of current treatment modalities. This review aims to offer clinicians and researchers a consolidated resource, highlighting the intricacies of MRONJ management and proposing directions for future research. Through this comprehensive approach, the paper seeks to advance the understanding and treatment of MRONJ, ensuring an evidence-based, patient-centric approach in clinical practice.

The purpose of this study is to explore the effects of dual-task training, including cognitive tasks, on cognitive and bodily functioning and β-amyloid levels in Alzheimer's dementia patients. The subjects were 34 inpatients diagnosed with Alzheimer's dementia at a nursing hospital located in Gyeongsansi, South Korea. The patients were randomly divided into a dual-task group (n = 16) and a single-task group (n = 18). The dual-task group performed cognitive tasks at the same time as exercising tasks, while the single-task group performed only exercise tasks. Each group was trained for 30 minutes three times a week for eight weeks. The Mini-Mental State Examination was used to measure the patients’ cognitive function. Static and dynamic balance were measured to evaluate bodily functioning. Static balance was measured using Biorescue, while dynamic balance was measured using the Berg Balance Scale. Blood analysis was performed to measure levels of β-amyloid, which is known to cause Alzheimer's dementia. Both groups exhibited statistically significant improvements in gait function after the training (p < .05). The dual-task group exhibited statistically significant differences in cognitive function, static and dynamic balance function, and β-amyloid levels after training (p < .05). A significant difference was observed between the two groups (p < .05). Dual-task activities were found to be effective in improving cognitive and bodily functioning and reducing β-amyloid levels in Alzheimer's dementia patients. Therefore, dual-task training is thought to be an effective method of treating and preventing Alzheimer's dementia.

The Boltzmann-Gibbs (BG) entropy has been used in a wide variety of problems for more than a century. It is well known that BG entropy is extensive, but for certain systems such as those dictated by long-range interactions, the entropy must be non-extensive. Tsallis entropy possesses non-extensive characteristics, which is parametrized by a variable q (q = 1 being the classic BG limit), but unless q is determined from microscopic dynamics, the model remains but a phenomenological tool. To this date very few examples have emerged in which q can be computed from first principles. This paper shows that the space plasma environment, which is governed by long-range collective electromagnetic interaction, represents a perfect example for which the q parameter can be computed from micro-physics. By taking the electron velocity distribution function measured in the heliospheric environment into account, and considering them to be in quasi equilibrium state with electrostatic turbulence known as the quasi-thermal noise, it is shown that the value corresponding to q = 9/13 = 0.6923 may be deduced. This prediction is verified against observation made by spacecraft, and it is shown to be in excellent agreement.

Wireless Sensor Network is an emerging technology and the collaboration of wireless sensors becomes one of the active research areas to utilize sensor data. Various sensors collaborate to recognize the changes of a target environment, to identify, if occurs, any radical change. For the accuracy improvement, the calibration of sensors has been discussed, and sensor data analytics are becoming popular in research and development. However, they are not satisfactorily efficient for the situations where sensor devices are dynamically moving, abruptly appearing or disappearing. If the abrupt appearance of sensors is a zero-day attack and the disappearance of sensors is an ill-functioning comrade, then sensor data analytics of untrusted sensors will result in an indecisive artifact. The pre-defined sensor requirements or meta-data based sensors verification is not adaptive to identify dynamically moving sensors. This paper describes a deep-learning approach to verify the trustworthiness of sensors by considering the sensor data only, without having to use meta-data about sensors or to request consultation from a cloud server. The contribution of this paper includes 1) quality preservation of sensor data for mining analytics and 2) authenticity verification of dynamically moving sensors with no external consultation.

Research on piezo-composite actuators has been actively conducted over the past two decades as a response to strong demand for light, compact actuators to replace electro-magnetic motor actuators in micro robots, small flying drones, and compact missile systems. Layered piezo-composite unimorph actuators have been studied to provide active vibration control of thin-walled aerospace structures, control the shapes of aircraft wing airfoils, and control the fins of small missiles, because they require less space and provide better frequency responses than conventional electro-magnetic motor actuator systems. However, based on the limited actuation strains of conventional piezo-composite unimorph actuators with poly-crystalline piezoelectric ceramic layers, they have not been implemented effectively as actuators for small aerospace vehicles. In this study, a lightweight piezo-composite unimorph actuator (LIPCA-S2) was manufactured and analyzed to predict its flexural actuation displacement. It was found that the actuated tip displacement of a piezo-composite cantilever could be predicted accurately using the proposed prediction model based on the nonlinear properties of the piezoelectric strain coefficient and elastic modulus of a piezoelectric single crystal.

Energy and Environment Tax is used globally as a means of environment policy. Energy and Environment tax plays an important role as a driving force for the conversion to environmentally friendly energy. The basic tax principles considered in the design of energy and environmental taxation increases the efficiency of policy instruments. The purpose of this study is to evaluate South Korea’s energy and environmental taxation based on tax principles, namely, equity, efficiency, simplicity, flexibility, and accountability and to suggest directions for improvement. This study applied a methodology that provides policy implications, such as reviewing existing literature and comparing energy and environmental taxes. Results of this study show that South Korea’s energy and environmental taxation is negative in terms of equity, simplicity, and accountability. South Korea’s current energy and environmental tax is regressive to income classes and complex tax structures and it does not objectively measure the impact of energy and environmental taxation. However, energy and environmental taxation is evaluated positively in terms of efficiency and flexibility because it meets the greenhouse gas reduction policy and operates a flexible tax rate. The results of this analysis provide policy implications for reorganizing South Korea’s energy and environmental taxation

Cardiovascular-related diseases are one of the leading causes of death worldwide. An understanding of heart movement based on images plays a vital role in assisting the procedure in the postoperative and postoperative processes. In particular, if the shape information can be provided in real-time using the electrocardiogram(ECG) signal using this information, the heart’s movement information can be used for cardiovascular analysis and imaging guides during surgery. In this paper, we propose creating a 3D+t cardiac coronary artery model that is rendered in real-time according to the ECG signal. Hierarchical cage-based deformation modeling is used to generate mesh deformation used during the procedure according to the ECG signal. We match the blood vessel’s lumen obtained from the ECG-gated 3D+t CT angiography taken at the multiple cardiac phases to derive the optimal deformation. Splines for 3D deformation control points were used to continuously represent the obtained deformation at the multi-view according to the ECG signal. To verify the proposed method, we compared the manually segmented lumen and results of the proposed method for eight patients. The average distance and dice coefficient between the two models was 0.543mm and 0.735, respectively. The required time for registration of the 3D coronary artery model is 23.53 seconds/model. rendering speed to derive the model according to the ECG signal after generating the 3D+t model is faster than 120 FPS.

This study numerically analyzed the effect of composition and temperature and oxygen concentration changes on the selfignition characteristics of n-heptane/methane blended fuels in order to cope with development of alternative fuels and international environmental regulations. CHEMKIN-PRO is used as the analysis program and the LLNL model is used as the reaction model. The numerical results revealed that on autoignition and ignition delay time were sensitive to changes in fuel composition and oxygen concentration. As the methane ratio increases, OH radicals causing ignition are delayed in generation and the concentration is lowered, thus longer the ignition timing. The oxygen concentration in the mixed fuels are changed to simulate the exhaust gas recirculation and a numerical analysis in then performed. As the oxygen concentration decreases, the ignition delay time longer because the nitrogen gas acts as a thermal load in the combustion chamber.

Energy taxation is used globally as a means of energy policy. Energy tax plays an important role as a driving force for the conversion to environmentally friendly energy. The basic tax principles considered in the design of energy taxation increases the efficiency of policy instruments. The purpose of this study is to evaluate South Korea’s energy taxation based on tax principles, namely, equity, efficiency, simplicity, flexibility, and accountability and suggest directions for improvement. This study applied a methodology that provides policy implications, such as reviewing existing literature and comparing energy taxes. Results of this study show that South Korea’s energy taxation is negative in terms of equity, simplicity, and accountability. South Korea’s current energy tax is regressive to income classes and complex tax structures and it does not objectively measure the impact of energy taxation. However, energy taxation is evaluated positively in terms of efficiency and flexibility because it meets the greenhouse gas reduction policy and operates a flexible tax rate. The results of this analysis provide policy implications for reorganizing South Korea’s energy taxation.

This study proposes a short- and medium-term electricity consumption prediction algorithm by combining the GRU model suitable for long-term forecasting and the Prophet model suitable for seasonality and event handling. (1) Manufacturing Company B's Electricity consumption data and meteorological data in Naju, Jeollanam-do, South Korea are collected and preprocessed. (2) The preprocessed data proposes the Prophet model in the first step for seasonality and event handling prediction. (3) In the second step, seven multivariate data are experimented with GRU. Specifically, the seven multivariate data consist of six meteorological data and the residuals between the predicted data from the proposed Prophet model in Step 1 and the observed data. These are utilized to predict electricity consumption at 15-minute intervals. (4) Electricity consumption is predicted for short-term (2 days and 7 days) and medium-term (15 days and 30 days) scenarios. The experimental results demonstrate that the proposed method outperforms the conventional Prophet model by more than 23 times and the modified GRU model by more than 2 times in terms of MAPE.

Powdery mildew disease, caused by Sphaerotheca fusca, is a major disease affecting cucumbers cultivated in greenhouses. This study was conducted to find defense genes induced by DL-3-amino-butyric acid (BABA) and powdery mildew in cucumber. The 2000 and 5000 mg/L BABA treated cucumber exhibited disease severity of 25% and 5%, respectively, whereas the water treated cucumber leaves were severely infected, with a disease severity of 90%. BABA did not effect spore germination of the powdery mildew pathogen,. showing BABA is not antifungal agent against the pathogen. In quantitative real-time PCR analysis, BABA treated cucumber upregulated transcriptional level of the defense genes CsPAL, CsPR3,CsPR1, CsLOX1, CsLOX23, Cs LecRK6.1, CsWRKY20, and Cupi4 in cucumber to maximum levels at 48 h, whereas CsLecRK6.1 reached maximum expression after 24 h and futher salicylic acid (SA) levels were significantly increased in BABA-treated cucumber plants. In addition, the infected cucumber with powdery mildew enhanced the expression levels from1.6 to 47.3 fold of the defense genes PAL, PR3, PR1, Lox1, Lox 23, LecRK6.1, WRKY20, and Cupi4 compared to heathy cucumber. The results suggests the BABA induced defense response is associated with SA signal pathway-dependent systemic acquired resistance (SAR) in cucumber, which is involved in plant resistance mechanisms.

We introduce the lower chaos grade of a real-valued function F defined on the Markov triple (E,μ,Γ), where μ is a probability measure and Γ is the carré du champ operator. As an application of this concept, we obtain the better estimate of the four moments theorem for Markov diffusion generators worked by Bourguin et al (2019). For our purpose, we need to find the largest number except zero in the set of eigenvalues corresponding to its eigenfunction in the case where the square of a random variable F, coming from a Markov triple structure, can be expressed as a sum of eigenfunctions, We give some examples of eigenfunctions of the diffusion generators such as Ornstein-Uhlenbeck, Jacobi and Romanovski-Routh. In particular, two bounds, called the four moments theorem and fourth moment theorem respectively, will be provided for the normal approximation of the case where a random variable F comes from eigenfunctions of a Jacobi generator.

Hypoeutectic Al-Si-Cu-Mg alloys are promising candidates for use in automobile parts. Therefore, various attempts have been made to develop alternative casting technologies to improve the mechanical properties of cast alloys. In this study, the effects of modification with different levels of strontium (Sr) on primary and eutectic silicon (Si) in hypoeutectic Al-Si-Cu-Mg alloys were investigated. Using a gravity cast process, the Sr-modified samples were prepared from melts with four levels (100, 200, 300, and 400 ppm) of Sr. In addition, the wear properties of the cast samples were studied using a block-on-ring wear-testing machine. The sizes of the primary Si phases increased with increasing Sr content in the alloys. Moreover, more finely dispersed eutectic Si was observed with higher Sr content. In frictional wear tests, the specific wear rates of the test specimens decreased as the Sr content increased from 200 to 400 ppm. Coarse Si phases are considered to act as solid lubricants in the contact region during sliding.

Using traditional methods based on detection rules written by human security experts, there is a significant challenge in accurately detecting many network threats that are increasingly becoming sophisticated. In order to deal with the limitation of the traditional methods, network threat detection techniques utilizing artificial intelligence technologies such as machine learning are being extensively studied. Research has also been conducted on analyzing various string patterns in network packet payloads through natural language processing techniques to detect attack intent. However, due to the nature of packet payloads containing binary data as well as text data, a new approach is needed that goes beyond typical natural language processing techniques. In this paper, we study a token extraction method optimized for payloads using n-gram and byte pair encoding techniques. Furthermore, we generate embedding vectors that can understand the context of the packet payload using algorithms such as Word2Vec and FastText. We also compute the embedding of various header data associated with packets, such as IP addresses and ports. Given the combination of these features, we ensemble a text 1D-CNN and a multi-head attention network in a novel fashion. We validated the effectiveness of our classification technique with the CICIDS2017 open dataset and over half a million data collected by The Education Cyber Security Center (ECSC) currently operating in South Korea. The proposed model showed remarkable progress compared to previous studies, demonstrating a highly accurate classification performance with an F1-Score of 0.998. Our model can also preprocess and classify 150,000 network threats per minute to help security agents in the field divert their time to analyzing more complex attack patterns.

(1)Background:The purpose of the study was conducted to investigate the recovery of heart rate according to the performance level and rest interval conditions during 65%1RM bench press exercise;(2)Methods:The subjects of this study were eight healthy male college studentsin their 20 (years) attending University C. 1RM was measured before theexperiment, and the exercise sequence of six rest intervals (30sec, 1, 2, 3, 4,5min) was randomly selected and cross-distribution experiment. The experimental measurement was conducted a total of six times according to the rest interval condition (six conditions) once every 3 days to minimize the learning effect. The data was verified with repeated measures Two-way ANOVA and Contrast's repeated method was applied for post-comparison;(3)Results: First, the amount of exercise and the number of repetitions statistically substantially dropped (p&lt;.001) as the number of sets rose, and a greater decline was shown when the rest periods got shorter. Second, the heart rate after exercise was increased significantly (p&lt;.01) as the set progressed in the long rest interval conditions (4, 5min), and the heart rate after rest was increased statistically significantly (p&lt;,05) as the set progressed in the short rest interval conditions (30sec, 1min). Third, the heart rate recovery rate (%) was able to perform more repetitions than in the short rest interval condition due to the relatively high physiological recovery of the neuromuscular and circulatory system under the long rest interval condition;(4)Conclusions: Therefore, according to the rest conditionduring the 65%1RM bench press exercise conducted in this study, the restinterval condition of 2~3min may be effective in improving muscular endurance,and the rest interval condition of 4~5min may be effective in improving muscular hypertrophy. As a result of this study, there was a significant difference in exercise performance (repetition) and physiological recovery depending on the rest interval condition according to the set progress during the same intensity resistance exercise. Through this, it is judged that it is possible to adjust the amount of exercise appropriate for the purpose of training by adjusting not only the intensity during resistance exercise but also the rest interval between sets.

With an increasing concern for global warming, there have been many attempts to reduce greenhouse gas emissions. About 30 % of total energy has been consumed by buildings and much attention has been paid to reducing building energy consumption. While there are many ways of reducing building energy consumption, accurate energy consumption prediction becomes more significant. As mechanical systems are the most energy-consuming components in the building, the present study developed the energy consumption prediction model for a direct-fired absorption chiller by using the ANN technique for the short term. The ANN model was optimized and validated with the actual data collected through a BAS. For the optimization, the numbers of input variables and neurons, and the data size of training were applied. By changing these parameters, the predictive performance was analyzed. In sum, the outcome of the present study can used to predict the energy consumption of the chiller as well as improve the efficiency of the energy management. The outcome of the present study can be used to develop a more accurate prediction model with a few datasets in that it can improve the efficiency of building energy management.

The development of technology that combines supercapacitors and lithium-ion batteries by externally connecting them in parallel is ongoing. This study examines the correlation between the volume ratio and electrical characteristics of a cell made by internally connecting a battery capacitor with Li4Ti5O12 as the anode active material and a supercapacitor in parallel. It was found that increasing the volume occupied by the battery capacitor in the cell led to increased cell energy and resistance, resulting in decreased output characteristics. Conversely, increasing the volume occupied by the supercapacitor in the cell led to a decrease in the IR drop during discharge and cell temperature when evaluating cycle characteristics with a current of 20C. The study also examined the behavior of the current distributed during the charging and discharging process based on the volume ratio of the supercapacitor and the battery capacitor. Analyzing the correlation between the volume ratio and electrical characteristics of supercapacitors and battery capacitors could potentially lead to the development of a new type of energy storage device.

In recent years, exterior-rotor brushless DC motors have become increasingly popular in robotics applications due to their compact shape and high torque density. However, these motors were originally used for continuous operation in drones. For applications such as exoskeletons, prostheses, or legged robots, short bursts of high power are often required. Unfortunately, vendors do not typically provide data on the motors' performance under these conditions. This paper presents experimental data on the torque-speed relationship, efficiency, and thermal responses of one of the most widely used outrunner-type brushless motors across its full operating range, including high-power, short-duration operation. The results of this study can inform the selection and design of actuators for a range of robotics applications, particularly those that require high power output for brief periods of time.

Polypyrrole (PPy)-capped caped silver nanowire (Ag NW) nanomaterials (core–shell rod-shaped Ag NW@PPy) were synthesized suing a one–port suspension polymerization technique. The thickness of PPy layer on the 50-nm-thickness/15-μm-length Ag NW was effectively controlled to 10, 40, 50, and 60 nm. Thin films cast from one-dimensional conductive Ag NW@PPy formed a three-dimensional (3-D) conductive porous network structure and provided excellent electrochemical performance. The 3-D Ag NW@PPy network can significantly reduce the internal resistance of the electrode and maintain structural stability. As a result, a high specific capacitance of 625 F/g at a scan rate of 1 mV/s was obtained from the 3-D porous Ag NW@PPy composite film. The cycling performance over a long period exceeding 10,000 cycles was also evaluated. We expect that our core–shell-structured Ag NW@PPy composites and their 3-D porous structure network films can be applied as electrochemical materials for the design and manufacture of supercapacitors and other energy storage devices.

This study examines the effect of a corporate citizenship-oriented high performance work system (CC-oriented HPWS) and the interactive influence of CC-oriented HPWS and person-organization fit (PO fit) on creativity at the individual level. Our analysis of field data collected from 303 employees in two branch companies with a two-phase collection method provides empirical support for our theoretical model. The results indicate that high performance work system (HPWS) reflecting corporate citizenship (CC) correlates positively with individual creativity. In addition, the moderating effect of PO fit is significant in the relationship between CC-oriented HPWS and creativity at the individual level. On this basis, this paper discusses the theoretical and practical implications of these findings for future research.

In the present study, piecewise integrated composite (PIC) bumper beam for passenger cars was proposed and design optimisation process for composite bumper beam against IIHS test was carried out with the help of machine learning. Several elements in IIHS bumper FE model have been assigned to be references, in order to collect training data which, allow the machine learning model to study the method of predicting loading types of each finite element. 2-D and 3-D implementations were provided by machine learning models, which determined stacking sequences of each finite element in PIC bumper beam. It was found that the PIC bumper beam, which was designed by machine learning model has direct impact on reducing the possibility of failure as well as increasing bending strength effectively than conventional composite bumper beam. Moreover, 3-D implementation produced better results compared with 2-D implementation since it was preferable to choose loading type information which was achieved from surroundings when the target elements were located either at corner or junction of planes instead of using information came from the same plane of target.

In this work, we report on the first attempt using size-controlled ZnO nanoparticles (NPs) to analyze role of thioacetamide (TAA) in forming ZnS nanostructures from ZnO. The size-controlled B(blue)_, G(green)_, Y(yellow)_ZnO QDs, and NC (nanocrystalline)_ZnO NPs were synthesized by sol-gel process as well as hydrothermal method, respectively and then reacted with an ethanolic TAA solution as sulfur source. After reacting with TAA, the formation of ZnS QDs was observed over time. The chemical reactions were identified using X-ray diffraction (XRD), Transmission Electron Microscope (TEM), UV-vis spectroscopy, and Photoluminescence (PL). All results indicate that ZnS formation is due to direct crystal growth and/or chemical conversion of ZnO to ZnS. Our results enable a broader understanding of the synthetic mechanisms involved in the use of TAA as a sulfur source in forming ZnS nanostructures from ZnO.

Liver cirrhosis leads to hepatic dysfunction and life-threatening conditions. Though clinical efficacy of autologous bone marrow-drived mesenchymal stem cells (BM-MSC) transplantation in alcoholic cirrhosis (AC) was demonstrated, the relevant mechanism has not been elucidated. We aimed to identify predictive factors and gene/pathways for responders after autologous BM-MSC transplantation. Fifty-five patients with biopsy-proven AC underwent autologous BM-MSC transplantation. The characteristics of responders who showed improvement in fibrosis score (≥ 1) after transplantation were compared with those of non-responders. BM-MSCs were analyzed with cDNA microarrays to identify genes and pathways that were differentially expressed in responder after transplantation. Thirty-three patients (66%) were responders. In the multivariate analysis, initial high Laennec score (p=0.007, odds ratio 3.73) and performance of BM-MSC transplantation (p=0.033, odds ratio 5.75) were predictive factors for responder. Three genes (olfactory receptor 2L8, microRNA4520-2, and chloride intracellular channel protein 3) were upregulated in responders and 11 metabolic pathways (inositol phosphate, ATP-binding cassette transporters, protein kinase signaling, extracellular matrix-receptor interaction, endocytosis, phagosome, hematopoietic cell lineage, adipocytokine, peroxisome proliferator-activated receptor, fat digestion/absorption, and insulin resistance) were upregulated in non-responders (p<0.05). BM-MSC transplantation is warranted treatment for AC patients with high Laennec score. Cell-based therapy utilizing response-relating genes or pathway can be treatment candidate.

Background: Little information exists regarding the differences in the clinical and laboratory characteristics of Kikuchi-Fujimoto disease (KFD) according to age. Objective: To evaluate the clinical and laboratory characteristics of KFD according to age. Methods: We retrospectively evaluated patients diagnosed with KFD at Pusan National University Hospital between 2010 and 2020. Results: Eighty patients (46 children and 34 adults) with a mean age of 21.5 ± 11.8 years (range, 3–49 years) were included in the study. Among children, the male sex ratio was higher, in adults, the female sex ratio was higher. Fever, tenderness in the lymph node, and skin rashes were more common in children, while myalgia and weight loss were more common in adults. In children, the recurrence rate was significantly higher among boys than among girls (15.8% vs 0.0%, P=0.001). EBV and ANA positivity rates were higher in boys than in girls. In adults, the recurrence rate was significantly higher in women than in men (18.2% vs 0.0%, P=0.005). ANA positivity rates were higher in women than in men. Conclusion: The clinical features, laboratory findings, and recurrence of KFD may differ depending on age and sex. Clinicians should be aware of this.

Knowledge-intensive business services (KIBS) are professional services that create new added value by creating, accumulating, and disseminating new knowledge. When it comes to conducting business involving new technology, KIBS play the roles of innovator, user, and producer of new technology that has led to technological innovation. This study aimed to determine the role of KIBS as a tool for innovation in a country’s economic system. Specifically, the degree and role of their impact on the Korean economy were analyzed and compared for the entire KIBS sector, T-KIBS (a new technology-based professional service), P-KIBS (a traditional professional service), and every subsector. For this purpose, the demand-inducement model, supply inducement model, and interlinkage effects method were applied using the 2019 input-output table published in 2022. The analysis showed that the indirect production inducement effect of the entire KIBS industry on other industries was 0.800 KRW, the indirect added value inducement effect was 0.330 KRW, and the supply disruption effect was 1.144 KRW. For T-KIBS, the indirect production inducement effect was 0.687 KRW, the indirect added value inducement effect was 0.272 KRW, and the supply disruption effect was 0.730 KRW. For P-KIBS, the indirect production inducement effect was 1.472 KRW, the indirect added value inducement effect was 0.646 KRW, and the supply disruption effect was 2.657 KRW. Finally, regarding the economic ripple effect of the KIBS subsector, legal and management support services and advertisements corresponding to P-KIBS showed higher figures than the T-KIBS subsectors in all sectors, including production inducement, the added value inducement effect, and the supply disruption effect. These results revealed that in the South Korean economic system, KIBS contribute to production and value addition across all industrial sectors. It is apparent that the absence of supply significantly disrupts other industries. Furthermore, production inducement effects are evenly distributed among all the KIBS subsectors in the secondary and tertiary sectors, while the value-added effects have a greater impact on the tertiary sector. In terms of the supply shortage effects, the secondary sector experiences a more significant impact. This underscores the crucial role of KIBS in sustaining and enhancing overall economic activity in South Korea. This study is significant in that it not only investigated KIBS as an industry group using the advantages provided by industry linkage analysis but also examined and compared detailed subsectors, thereby elaborately evaluating the influence relationship between KIBS and other industries. Therefore, the results presented in this study are expected to be useful for fostering the KIBS industrial sector and establishing economic innovation policies using KIBS.

Workplace bullying is a prevalent issue with a significant impact on employees’ mental health. This study aimed to explore the relationship between workplace bullying and the prevalence of depression with a particular focus on the role of gender. A total of 12,344 Korean employees aged 19–65 years were included in the study. They completed questionnaires including the Korean version of the Occupational Stress Scale, the Oldenburg Burnout Inventory, and the Center for Epidemiologic Studies Depression Scale (CES-D). A score of 16 or higher in CES-D indicated depression. The association between workplace bullying and depression was analyzed using logistic regression analyses. The average CES-D scores were higher for both male and female employees who experienced bullying than for those who did not (p &lt; 0.001). The association between the experience of workplace bullying and the prevalence of depression was statistically significant for both genders, with a stronger correlation observed among male employees (p for interaction &lt;0.001). Organizations are urged to address workplace bullying, particularly for male employees, through the implementation of anti-bullying strategies and policies, as well as the provision of mental health resources and support.

A growing number of companies in the brewery industry have made commitments to measure and reduce their greenhouse gas (GHG) emissions. However, many brewers, particularly craft brewers with relatively low rates of production, have struggled to meet these commitments. The purpose of this research was to investigate the challenges and benefits of measuring and reducing GHG emissions in the craft brewery industry. The research was conducted in Ontario, Canada, which has seen strong recent growth in the craft brewery industry. A case study and semi-structured interviews among Ontario Craft Brewers were conducted. The case study found that indirect (scope 3 emissions under the WBCSD & WRI GHG Protocol) GHG sources accounted for 46.4% of total GHGs, with major sources from barley agriculture, malted barley transportation, and bottle production. Direct emissions (scope 1) accounted for only 14.9% of GHGs, while scope 2 emissions, comprised mainly of energy consumption, accounted for 38.7% of GHGs. The case study and interviews found that the main challenges in calculating brewery GHGs are secondary data availability, technical knowledge, and finances. The study also found that the main benefits for Ontario breweries to measure their GHGs include sustainability marketing and preserving the environment. The interviews also found a poor understanding of carbon regulation among Ontario Craft Brewers, which is interesting considering that Ontario implemented a provincial cap and trade program in 2017.

This study aimed to learn disturbance information to effectively control multiple small unmanned surface vehicles (USVs) for marine research purposes at sea. The learned disturbance information was used to investigate the control of multiple USV swarms based on the leading–following formation technique. Numerous studies have already been conducted on swarm control using drones in the air; however, there has been a paucity of studies on swarm control in the sea. Contrary to swarm control in aviation, the fluid density of the sea is high, and the environment is constantly changing; thus, a robot system would be significantly affected by disturbances. This ultimately influences the maintenance of swarm formations and the accomplishment of missions [1,2]. Therefore, it is difficult to control numerous moving objects while maintaining a formation at sea by using the existing aerial drone swarm control technology. To solve this problem, we learned a long short-term memory (LSTM) model with time-series marine environmental data to predict maritime disturbances. Thereafter, the model was applied to a mission-based position control algorithm of each USV constituting the swarm to design a marine unmanned floating vessel swarm control system that can be applied to various marine environments. Specifically, to effectively control numerous small USVs for marine research purposes, ocean currents are learned using the LSTM algorithm, and the predicted ocean currents are used to generate a swarm USV control system for USV formations. In this study, a control system model of several small USVs equipped with two rear thrusters and a front lateral thruster was designed. The LSTM algorithm was learned using the previous ocean current data, and the velocity of the following ocean currents was predicted. The predictions were then used as a system disturbance to determine the thrust of the controller. To measure ocean currents in the sea when each USV moves, the velocity, azimuth, and position (latitude, longitude) data from the GPS mounted on the USV were used to measure the velocity and direction of the hull's movement. Further, the flow rate was measured using a flow rate sensor on a small USV. The USV movement and position were controlled using an artificial neural network-PID (ANN-PID) controller. This study entailed a comparative analysis of the designed USV model results and those generated by the simulator, including the behavior control rule of the USV swarm and the path of the actual USV swarm at sea. It was verified that the effectiveness of the USV mathematical model and behavior control rules. By comparing the movement path of the swarm USV with and without the disturbance learning algorithm and ANN-PID control algorithm applied to the designed simulator, the position error and maintenance performance of the swarm formation were analyzed, and the application results were compared.

With the increase of intelligent voice phishing and the increasing reliance on open banking systems, there has been a rise in cases where individuals' personal information has been exposed, resulting in significant financial losses for the victims. Non-face-to-face transactions in the financial sector face challenges such as customer identification, ensuring transaction integrity, and preventing transaction rejection. Blockchain-based distributed ledgers have been proposed as a solution, but their adoption is limited due to the difficulty of managing private keys and the burden of gas fees management. This paper proposes a non-face-to-face P2P real-time token payment system that minimizes the risk of key loss by storing private keys in a keystore file and database through a server-based key management module. The proposed system simplifies token creation and management through a server-based token management module and implements an automatic gas charging function for smooth token transactions. Transaction integrity and non-repudiation are ensured through a transaction confirmation module that uses transaction IDs without exposing personal information. Furthermore, advanced security measures such as blocking foreign IP access and DDoS defense are implemented to securely protect user data. The proposed system aims to provide a convenient, secure, and accessible online payment solution to the public by implementing a self-authentication function using a web application that is not limited to smart phones or application platforms.

The weakness of the dispersion-managed link combined with optical phase conjugation to compensate for optical signal distortion caused by chromatic dispersion and nonlinear Kerr effect of standard single mode fiber is its limited structural flexibility. We propose dispersion map that can simultaneously compensate for the distorted wavelength division multiplexed signal while increasing the configurational flexibility. Each residual dispersion per span (RDPS) in the former half of the proposed link is randomly determined, and in the latter half, the arrangement order of RDPS is the same as or inverted in the former half. We confirm that the dispersion maps in which the RDPS distribution pattern in the latter half is opposite to the arrangement order in the former half are more effective in compensation, and rather, the compensation effect is better than in the dispersion map of the conventional scheme. The notable result of this paper is that the increase of flexibility can be achieved through random arrangement of RDPS in the former half, and the compensation improvement can be achieved by through inverse arrangement in the latter half which make the distribution profile of each half link roughly symmetric with respect to the midway optical phase conjugator.

Refinery planning optimization is a challenging problem as regards handling the nonconvex bilinearity mainly due to pooling operations in processes such as crude oil distillation and product blending. This work investigates the performance of several representative piecewise-linear (or piecewise-affine) relaxation schemes (referred to as McCormick, bm, nf5, nf6t, and de (which is a new approach proposed based on eigenvector decomposition) that mainly give rise to mixed-integer optimization programs to convexify a bilinear term using predetermined univariate partitioning for instances of uniform and non-uniform partition sizes. Computational results show that applying these schemes give improved relaxation tightness than only applying convex and concave envelopes as estimators. Uniform partition sizes typically perform better in terms of relaxation solution quality and convergence behavior. It is also seen that there is a limit on the number of partitions that contributes to relaxation tightness, which does not necessarily correspond to a larger number of partitions, while a direct relation between relaxation size and tightness does not always hold for non-uniform partition sizes.

A highly crystallized monoclinic-scheelite type BiVO4 powders were successfully synthesized by solvothermal method. The as-synthesized BiVO4 powders were characterized by XRD, FE-SEM, Raman spectroscopy, UV-vis DRS spectroscopy and TA-PL. From the XRD data and Raman spectra, the monoclinic-scheelite phase BiVO4 sample can be obtained at higher solvothermal synthesis temperature more than 140 oC. The preparation conditions such as, the Bi/V molar ratio and synthesis temperature, have significantly effects on the morphologies of the BiVO4 samples. BVO2 sample shows the highest PL peak, which has the highest formation rate of OH radicals and the highest photocatalytic activity. This result suggests that the formation rate of OH radicals shows a good correlation with the photocatalytic activity.

Background Despite the high vaccination coverage rate, in-hospital transmission of measles continues to occur in South Korea. We present a measles outbreak in which two healthcare workers (HCWs) with presumptive evidence of measles immunity were infected by a patient with typical measles at a single hospital in South Korea. This facilitated the evaluation of measles seroprevalence in all HCWs. Methods In 2018, suspected patients and contacts exposed during a measles outbreak were investigated based on their medical histories and vaccination status. Cases were confirmed by the detection of measles-specific immunoglobulin M or RNA. After the measles outbreak, all HCWs underwent measles IgG testing for point-prevalence surveillance. In addition, we have routinely performed measles IgG tests on new HCWs since 2019. The measles vaccine was administered to HCWs who tested negative or equivocally negative for IgG antibodies. Results An index patient who returned from China with fever and rash was diagnosed with measles at a hospital in Korea. Two additional HCWs were revealed as measles cases: one was vaccinated with the 2-dose measles–mumps–rubella (MMR) vaccine, and the other, who was born in 1967, was presumed to have immunity from natural infection in South Korea. All three patients harbored the same D8 genotype. No additional measles cases were identified among the 964 contacts of secondary patients. After the measles outbreak, 2,310 HCWs underwent measles IgG tests. The average age at the time of the test was 32.6 years, and 74.3% were female. The overall seropositivity of measles was 88.9% (95% confidence interval, 87.5 –90.1). Although the birth cohorts between 1985 and 1994 were presumed to have received the measles-rubella (MR) catch-up vaccination in 2001, 175 (89.3%) HCWs were born after 1985 among the 195 seronegative cases. Conclusion Despite high population immunity, imported measles transmission occurred among HCWs, with presumed immunity. This report underscores the importance of understanding the prevalence of measles susceptibility among newly employed HCWs. This is important for policymaking regarding hospital-wide vaccinations to prevent vaccine-preventable diseases.

To decrease oral pathogens and increase the number of beneficial bacteria, probiotics and bioactive compounds made via microbial bioconversion are recently used. In addition, the interest regarding probiotics-mediated bioconversion with popular medicinal plants is increasing. These compounds produced during the process exhibit synergistic effect and enhance a variety of biological activities including pathogen inhibition in host. Therefore, we developed bioconverted milk with high inhibitory activity against oral pathogens and α-glucosidase. Lactiplantibacillus plantarum SMFM2016-RK was chosen as the probiotic due to its beneficial characteristics such as high acid and bile tolerance, antioxidant activity, and α-glucosidase inhibition. Based on the minimal bactericidal concentration against three oral pathogens, the following appropriate concentrations of Artemisia herba-alba extract were added to milk: 5 mg/mL of A. herba-alba ethanol extract and 25 mg/mL of A. herba-alba hot-water extract. Four bioconverted milks (BM), BM1, BM2, BM3, and BM4, were produced by combining L. plantarum SMFM2016-RK alone, L. plantarum SMFM2016-RK and ethanol extract, L. plantarum SMFM2016-RK and hot-water extract, and L. plantarum SMFM2016-RK with both extracts. As a result of antimicrobial activity, BM3 inhibited the growth of Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis the most, and BM4 suppressed the growth of Fusobacterium nucleatum the most. In addition, bioconverted milk containing A. herba-alba (BM2, BM3, and BM4) inhibited α-glucosidase more effectively than BM1. In conclusion, A. herba-alba-added milk bioconverted by L. plantarum SMFM2016-RK displayed the growth inhibitory effect on oral pathogens and the α-glucosidase inhibitory activity, thus it necessitates to evaluate the effects on the alleviation of oral diseases and glycemic control through future animal experiments.

BMAP-18, derived from the N-terminal region of bovine myeloid antimicrobial peptide BMAP-27, demonstrated potent antimicrobial activity without cytotoxicity. This study aimed to compare the antibacterial, antibiofilm, and anti-inflammatory properties of BMAP-18, rich in aromatic phenylalanine residues, with its aliphatic analogue, BMAP-18-FL. Both aromatic BMAP-18 and aliphatic BMAP-18-FL exhibited equally potent antimicrobial activities against Gram-positive and Gram-negative bacteria, particularly methicillin-resistant Staphylococcus aureus (MRSA) and multidrug-resistant Pseudomonas aeruginosa (MDRPA). Mechanistic investigations employing SYTOX green uptake, DNA binding, and FACScan analysis revealed that both peptides acted by inducing membrane permeabilization and subsequent intracellular targeting. Moreover, both BMAP-18 and BMAP-18-FL effectively prevented biofilm formation and eradicated existing biofilms of MRSA and MDRPA. Notably, BMAP-18-FL displayed superior anti-inflammatory activity compared to BMAP-18, significantly reducing the expression levels of pro-inflammatory cytokines in lipopolysaccharide-stimulated macrophages. This study emphasizes the similarities and differences in the antimicrobial, antibiofilm, and anti-inflammatory properties between aromatic BMAP-18 and aliphatic BMAP-18-FL, providing valuable insights for the development of multifunctional antimicrobial peptides against drug-resistant bacteria.

This study was conducted to investigate the correlations between social-emotional competence (SEC) and academic achievement (AA) among nursing students and to compare students’ level of each core skill of SEC (critical thinking disposition, self-directed learning, creativity, emotional intelligence, problem-solving, and collaboration) and academic achievement (clinical performance and subjective academic achievement). A cross-sectional design was adapted. Data were collected from 195 nursing students in the junior and senior years who had participated in clinical practicum from four universities in South Korea. General characteristics, levels of critical thinking disposition, self-directed learning, creativity, emotional intelligence, problem-solving, collaboration, and academic achievement were collected via self-reported questionnaire. Canonical correlation analysis was performed to evaluate the relationship between SEC and AA. The canonical correlation coefficient between SEC and AA was .762. Critical thinking disposition (Rs = .89), problem-solving (Rs = .86), cooperation (Rs = .80) made the most important contributions to SEC. Clinical performance (Rs=.95) and subjective AA (Rs=.57) were correlated with AA. SEC should be addressed to improve the AA of nursing students. All core skills of SEC should be regularly promoted. It is particularly urgent for nursing students to improve their creativity.

A composite beam is a structural member that behaves as a single unit by using shear connectors between a concrete slab and an H-shaped steel girder. The composite ratio is crucial and determined by the shear connectors' ability to withstand the horizontal shear forces between the concrete and steel girder. In this study, a U-shaped composite beam was designed, which differs from conventional composite beams as it allows the use of a steel girder as a formwork. Moreover, angle-type shear connectors, instead of stud-type connectors, were employed. Based on this design, large-scale U-shaped composite beams with angle-type shear connectors were fabricated, and load tests were conducted to analyze the behavior after composite action and the influence of shear connector spacing. Additionally, the strength of the angle-type shear connectors used in this paper was evaluated through finite element analysis. Finally, a strength evaluation method for composite beams of this configuration was proposed.

The objective of the this study was to reduce the benzo[a]pyrene-induced hepatotoxicity method in herbal medicine products and to give proof that neferine, daidzein, genistein possess antihepa-totoxic effects. B[a]P which classified as a group 1 carcinogen and metabolized to B[a]P-7,8-dihydrodiol-9,10-epoxide (BPDE) causes mutagenic DNA addition products. The reduc-tion of BPDE-DNA adduct formation by B[a]P-7,8-dihydrodiol-9,10-epoxide (BPDE) was derived by benzo[a]pyrene (B[a]P). In HepG2 cells, B[a]P exhibited toxicity and substance treatment of the cells with B[a]P with neferine in lotus and daidzein, genistein in soybean reduced the BPDE-DNA ad-ducts level. The level of B[a]P-metabolites in the substance treatment of the cells was presented that BPDE levels were reduced by neferine in lotus and daidzein, genisten in soybean. These results suggest that neferine in lotus and daidzein, genistein in soybean prevent B[a]P-induced hepato-toxicity for BPDE-DNA adduct formation.