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Objectives We analyzed the correlation between the infectivity and transmissibility of the severe acute respiratory syndrome coronavirus 2 Omicron sublineages BA.1, BA.2, BA.4, and BA.5. Methods We assessed viral replication kinetics and infectivity at the cellular level. Nasopharyngeal and oropharyngeal specimens were obtained from patients with coronavirus disease 2019, confirmed using whole-genome sequencing to be caused by the Omicron sublineages BA.1, BA.2, BA.4, or BA.5. These specimens were used to infect Vero E6 cells, derived from monkey kidneys, for the purpose of viral isolation. Viral stocks were then passaged in Vero E6 cells at a multiplicity of infection of 0.01, and culture supernatants were harvested at 12-hour intervals for 72 hours. To evaluate viral replication kinetics, we determined the cycle threshold values of the supernatants using real-time reverse transcription polymerase chain reaction and converted these values to genome copy numbers. Results The viral load was comparable between BA.2, BA.4, and BA.5, whereas BA.1 exhibited a lower value. The peak infectious load of BA.4 was approximately 3 times lower than that of BA.2 and BA.5, while the peak load of BA.2 and BA.5 was about 7 times higher than that of BA.1. Notably, BA.1 demonstrated the lowest infectivity over the entire study period. Conclusion Our results suggest that the global BA.5 wave may have been amplified by the higher viral replication and infectivity of BA.5 compared to other Omicron sublineages. These analyses could support the rapid assessment of the impact of novel variants on case incidence.

Mobile network traffic volume is increasing significantly every year, which could become a burden on the backbone of the 5G and beyond network in the future. Network caching has been a promising solution to improve quality of service (QoS) by reducing backhaul traffic and network latency. This paper studies network caching for private networks specialized for specific use cases in 5G. We adopt the Content-Centric Network (CCN) as the core network to improve caching efficiency, and suggests a Deep Q-Network (DQN) based method that jointly addresses content allocation strategy and cache replacement policy. Unlike previous studies, the proposed method can handle content and cache storage of heterogeneous sizes by implementing two-bit channel state generator which maps different-sized environment data to a fixed-sized image format. Simulation results shows that the proposed system outperforms existing cache systems by 20% to 50% in terms of cache hit ratio and network latency.

The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have been recognized as predictors of various critical illnesses. Our study aimed to investigate whether the NLR and PLR measured at different timepoints could predict poor neurological outcomes at 6 months. This observational retrospective cohort study included adults who had experienced out-of-hospital cardiac arrest (OHCA) and received targeted temperature management between November 2015 and December 2020. Patients with an active infection, as confirmed by an initial blood culture, were excluded. Multivariate logistic regression models were used to determine the association between the NLR and PLR at 0, 24, and 48 h after return of spontaneous circulation and poor neurological outcomes, defined as a Cerebral Performance Category score of ≥3 at 6 months. The NLR at 24 h, but not the NLR or PLR at other timepoints, was significantly associated with poor neurological outcomes (odds ratio: 1.05; 95% CI: 1.01–1.09; p = 0.018). The NLR at 24 h showed moderate accuracy in predicting poor neurological outcomes, with an AUC of 0.619. A cutoff value of 9.0 achieved 72.5% sensitivity and 47.7% specificity. The NLR measured at 24 h after ROCS could be used for early neuroprognostication given its low cost and widespread availability.

Objectives Coronavirus disease 2019 (COVID-19) has been declared a global pandemic owing to the rapid spread of the causative agent, severe acute respiratory syndrome coronavirus 2. Its Delta and Omicron variants are more transmissible and pathogenic than other variants. Some debates have emerged on the mechanism of variants of concern. In the COVID-19 wave that began in December 2021, the Omicron variant, first reported in South Africa, became identifiable in most cases globally. The aim of this study was to provide data to inform effective responses to the transmission of the Omicron variant. Methods The Delta variant and the spike protein D614G mutant were compared with the Omicron variant. Viral loads from 5 days after symptom onset were compared using epidemiological data collected at the time of diagnosis. Results The Omicron variant exhibited a higher viral load than other variants, resulting in greater transmissibility within 5 days of symptom onset. Conclusion Future research should focus on vaccine efficacy against the Omicron variant and compare trends in disease severity associated with its high viral load.

Research on prognostic factors for good outcomes in out-of-hospital cardiac arrest (OHCA) survivors is lacking. We assessed whether normal levels of normal neuron-specific enolase (NSE) value would be useful for predicting good neurological outcomes in comatose OHCA survivors treated with targeted temperature management (TTM). This registry-based observational study with consecutive adult (≥18 years) OHCA survivors with TTM who underwent NSE measurement 48 hours after cardiac arrest was conducted from October 2015 to November 2022. Normal NSE values defined as the upper limit of the normal range by the manufacturer (NSE

Reviews left by consumers on e-commerce platforms provide crucial marketing information as they are a publicly available source of information providing insight into consumers’ thoughts and opinions. However, it is physically impossible to read hundreds of reviews per product. Therefore, algorithmically extracting the necessary insights from a large volume of review data can provide more advanced information while reducing time and costs. This study aims to automate the process of extracting related products, pros and cons of products, and trend forecasting from review data using clustering algorithms. The review dataset for pros and cons of products and related products was constructed by selecting 17 products on the Naver Shopping platform and crawling them and the product keyword and search volume dataset for trend forecasting was constructed by crawling the data from Itemscout and Naver datalab platform. Various clustering-based algorithms such as Deep Clustering Network, BERTopic, and a Transformer-based forecasting model were used to conduct the research. It is expected that this will allow for a more accurate understanding of consumer thoughts, which can be utilized in marketing for various products and services.

Abstract We investigated the effect of specific surface area on the electrochemical properties of NiCo2O4 (NCO) for glucose detection. NCO nanomaterials with controlled specific surface areas were prepared by additive-assisted hydrothermal synthesis, and self-assembled nanostructures with urchin-, pine-needle-, tremella-, and flower-like morphologies were obtained. The novelty of this method is the systematic control of chemical reaction routes assisted by the addition of different additives during synthesis, which results in the spontaneous formation of various morphologies without any difference in the crystal structure and chemical states of the constituent elements. Such morphological control of NCO nanomaterials leads to considerable changes in the electrochemical performance for glucose detection. Combined with materials characterization, the relationship between the specific surface area and the electrochemical performance is discussed for glucose detection. This work can provide scientific insights for tailoring the surface area of nanostructures, which determines their functionality for potential applications in glucose biosensors.

This article investigates the source of booming noise emanating from the tailgate of an electric sport utility vehicle (SUV), along with proposed strategies to mitigate it. This annoying low-frequency booming noise, which significantly impacts interior sound quality, is less perceptible in conventional internal combustion engine vehicles. However, this noise is more readily detected in electric SUVs, highlighting the necessity for focused measures to reduce it. This study involved the measurement of booming noises during on-road vehicle tests to pinpoint their origins. Additionally, ODSs were extracted from the tailgate vibration signals to gain insight into its dynamic behavior. Modal tests were conducted on the tailgate to determine its dynamic characteristics and compared with driving test results to reveal the mechanism responsible for tailgate-induced booming noise. It was established that such noise is primarily due to the tailgate modes, resulting from a combination of rigid body motion in the fore-aft direction and deformation in the central section of the panel. An analytical model of the tailgate was developed using commercial finite-element analysis software to propose measures for reducing booming noise. Experimental findings validated this model’s accuracy. Structural enhancements were implemented to enhance the panel stiffness and improve the connection between the vehicle and tailgate via bushings to reduce the booming noise resulting from tailgate motion. Under random force inputs, the analytical results demonstrated a 13.8% reduction in maximum deformation in the tailgate model in the improved structural configuration with increased panel stiffness. This study identifies the mechanism generating booming noise, establishes a practical and simple dynamic model, and proposes improvement measures aimed at reducing the booming noise.

Objectives This study aims to identify the changes and statistical characteristics of the observed concentration data of fine particles (PM-2.5), one of the air environment indicators, and to analyze the variability of observation data in South Korea based on the statistical significance of the observation data. Methods We analyzed the changes and characteristics of daily average PM-2.5 observations based on descriptive statistics (the basic statistical and frequency analysis), and inference statistics (analysis of variance and post-hoc analysis) from December to March for the period 2015 to 2021. Results and Discussion Through ANOVA analysis and post-hoc analysis, it was confirmed that there was a statistically significant difference in the observed PM-2.5 as of the winter of 2019. Before 2019, the frequency of observed data greater 30 µg/m3 was relatively more than after 2019, and on the contrary, the frequency of observed data less than 20 µg/m3 was smaller than after 2019. After 2019, the frequency of high-concentration observations greater than 50 µg/m3 was insignificant, and on the contrary, the frequency of observations less than 20 µg/m3 was more than before 2019. As a result, It’s an average difference of 7.5 µg/m3 between before and after 2019, and it was reduced by about 24% compared to the average before 2019. Meanwhile, as a result of examining the weather influence (wind direction and wind speed) to analyze the causes of the observed data differences before and after 2019, no significant differences were identified between the two periods due to the influence of meteorological conditions. Conclusion The observed concentration of PM-2.5 data set in South Korea showed variability as of the winter season of 2019 at the significance level of 5%. The variability could be more attributed to anthropogenic activities and socio-environmental changes such as COVID-19 rather than natural environmental factors such as meteorological factors. In order to more effectively manage air quality, it is necessary to identify social changes and their flows, and at the same time conduct continuous and systematic research such as data expansion and analysis technique development.

Abstract Thin film-based optical sensors have been attracting increasing interest for use in developing technologies such as biometrics. Multilayered dielectric thin films with different refractive indices have been utilized to modulate the optical properties in specific wavelength bands for spectral selectivity of Thin Film Narrow Bandpass Filters (TFNBFs). Progress in TFNBF design has been made with the incorporation of metallic thin films. Narrower bandwidths with higher transmittance have been achieved in specific spectral bands. In this work, Ti/TiO2/SiO2 based multilayer thin films were prepared using pulsed-DC reactive sputtering. Computer simulations using the Essential Macleod Program allowed the optimal number of layers and thickness of the multilayer thin films to be determined to efficiently tailor the optical path transmitting specific wavelength bands. The addition of Ti metal layers within dielectric (TiO2/SiO2) multilayer thin films significantly changes the cutoff frequency of transmittance at specific wavelengths. Representative 26 multilayer films consisting of Ti, TiO2, and SiO2 show lower transmittance of 10.29% at 400 nm and 10.48% at 680 nm. High transmittance of 80.42% at 485 nm was observed, which is expected to improve the spectral selectivity of the TFNBF. This work provides a contribution to future simulation based design strategy based on experimental thin film engineering for potential industrial development opportunities such as optical biometrics.

There are three Rap proteins in Dictyostelium. RapA is a key regulator of cell adhesion and cytoskeletal rearrangement. Recently, RapC has been reported to be involved in cytokinesis, cell migration, and multicellular development. Here, we compare the functions of RapA and RapC using cells expressing or lacking Rap proteins, and confirm that RapA and RapC have opposite functions in cell spreading, adhesion, and migration. On the other hand, RapC has a unique function in cytokinesis and multicellular development. Activated RapA appears to stimulate spreading and adhesion of the cells to the substrate, possibly resulting in a decrease in the migration speed of the cells during chemotaxis without affecting the directionality, whereas RapC suppresses cell spreading and adhesion, thereby increasing the migration speed. Cells lacking RapC were defective in cytokinesis and multicellular development and showed multinucleation and formation of multiple tips from a mound during development. At the C-terminus, RapC has an additional stretch of amino acids, which is not found in RapA. The mechanism through which RapA and RapC perform their opposite functions in diverse cellular processes should be characterized further to understand the Rap signaling pathways in detail.

Upper gastrointestinal bleeding (UGIB) is a major cause of clinical deterioration worldwide. A large number of patients with UGIB cannot be diagnosed through endoscopy, which is normally the diagnostic method of choice. Therefore, this study aimed to investigate the diagnostic value of multi-detector computed tomography (MDCT) for patients with suspected UGIB. In this retrospective observational study of 386 patients, we compared contrast-enhanced abdominopelvic MDCT to endoscopy to analyze the performance of MDCT in identifying the status, location of origin, and etiology of UGIB. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were examined. In the assessment of bleeding status, MDCT was able to accurately identify 32.9% (21.9-43.9, 95% confidence interval [CI]) of patients with active bleeding, 27.4% (18.9-35.9, 95% CI) of patients with recent bleeding, and 94.8% (91.8-97.8, 95% CI) of patients without bleeding evidence (P

The industrial sector accounts for a significant proportion of total energy consumption. Factory Energy Management Systems (FEMSs) can be a measure to reduce energy consumption in the industrial sector. Therefore, machine learning (ML)-based electricity and liquefied natural gas (LNG) consumption prediction models were developed using data from a food factory. By applying these models to FEMSs, energy consumption can be reduced in the industrial sector. In this study, the multilayer perceptron (MLP) algorithm was used for the artificial neural network (ANN), while linear, radial basis function networks and polynomial kernels were used for support vector regression (SVR). Variables were selected through correlation analysis with electricity and LNG consumption data. The coefficient of variation of root mean square error (CvRMSE) and coefficient of determination (R2) were examined to verify the prediction performance of the implemented models and validated using the criteria of the American Society of Heating, Refrigerating, and Air-Conditioning Engineers Guideline 14. The MLP model exhibited the highest prediction accuracy for electricity consumption (CvRMSE: 17.35% and R2: 0.84) and LNG consumption (CvRMSE: 12.52% and R2: 0.88). Our findings demonstrate it is possible to attain accurate predictions of electricity and LNG consumption in food factories using relatively simple data.

This paper presents a study on the idle vibration reduction of a diesel sport utility vehicle (SUV). To reduce idle vibration, the transmission paths of vibration from the engine to the driver seat floor were investigated with the vehicle components related to idle vibration. Furthermore, operational deflection shape (ODS) tests were conducted to visualize the vibration shapes during engine idling. Experimental modal analyses were performed to obtain the natural frequencies and mode shapes. Through the ODS and modal tests, the vibration characteristics of the diesel SUV during idling were identified. Considering these vibration characteristics, a multi-body dynamic model for the diesel SUV described by differential equations of motion was established to evaluate the idle vibration. To implement the dynamic model effectively, the equivalent stiffnesses and damping coefficients included in the model were determined experimentally or analytically. The established dynamic model was verified by comparing the natural frequencies and idle vibration levels between simulations. Using this dynamic model, we analyzed the effects of various design variables on idle vibration and obtained an optimal design for reducing the idle vibration level. Finally, we present a design guide to reduce the idle vibration for diesel SUVs.

This paper presents the results of experimental investigation on the elastic–plastic response of steel unstiffened wedges with dead-rise 10° subjected to repeated impulsive pressure loadings. Repeated drop tests were performed with both wedge thickness and drop height varied. The pressure and histories were recorded during the tests and the permanent deflections were measured after every drop. Using the recorded test result, the effects of flexibility of wedges and repetition have been investigated. From the pressure history obtained from the tests the characteristics of the impulses were identified. Numerical simulations of the tests were made using the measured pressure history and the permanent deflection predictions were compared with those of the experiments. Keywords: Slamming phenomena, Impulsive pressure load, Wet free drop test, Dead-rise angle 10° bodies, Accumulated damage

In this study, the Taguchi experimental design method using an L16 orthogonal array was attempted in order to investigate the optimal operating conditions for the CO2 methanation process in Ni-based catalysts. The relative influence of the main factors affecting CO2 conversion and CH4 yield was ranked as follows: reactor pressure > space velocity > reaction temperature. The optimal combination of operating conditions was a reactor temperature of 315 °C, a pressure of 19 bar, and a space velocity of 6000 h−1. The effect of the H2/CO2 ratio on CO2 conversion and CH4 yield was further considered under these optimal operating conditions. Moreover, the catalyst was characterized in order to investigate the production of coke through Brunauer–Emmett–Teller analysis, thermogravimetric analysis, and scanning electron microscopy. The amount of coke produced after the reaction for approximately 24 h was ~2 wt.%. Therefore, the desired CH4 yield and long-term operational stability were successfully obtained using the Taguchi design method and catalyst characterization.

Solid fuelization technology can increase the heating value of sewage sludge such that it can be utilised as a fossil fuel substitutes. Reducing landfilling of bottom and fly ash resulting from heavy metals contained in sewage sludge is challenging. Hence, combustion melting technology (CMT), which can discharge bottom ash in the form of slag, has been proposed herein as an alternative to the conventional incineration technology. However, further research is required to improve the flowability of slag. Applicability of CMT for the stable treatment of heavy metals in the ash generated during the energisation of sewage sludge solid fuel has been reviewed. The change in the degree of fluidity was identified via a laboratory-scale fluidity measurement experiment following changes in melting temperature, mixing ratio of sewage sludge and sawdust, and basicity. The pouring index (PI) of sewage sludge solid fuel (pellet) was maintained at a level of about 60% at a basicity index of 0.8. Based on the results, the slagging rates and volume reduction rates, exhaust gas analysis, and heavy metal elution characteristics under oxygen enrichment were derived from a 2 ton/day combustion melting pilot plant experiment; thereafter, the feasibility of combustion melting of sewage sludge solid fuel was determined.