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Abstract Innate lymphoid cells (ILCs) are a heterogeneous population that play diverse roles in airway inflammation after exposure to allergens and infections. However, how ILCs respond after exposure to environmental toxins is not well understood. Here we show a novel method for studying the heterogeneity of rare lung ILC populations by magnetic enrichment for lung ILCs followed by particle-templated instant partition sequencing (PIP-seq). Using this method, we were able to identify novel group 1 and group 2 ILC subsets that exist after exposure to both fungal allergen and burn pit-related constituents (BPC) that include dioxin, aromatic hydrocarbon, and particulate matter. Toxin exposure in combination with fungal allergen induced activation of specific ILC1/NK and ILC2 populations as well as promoted neutrophilic lung inflammation. Oxidative stress pathways and downregulation of specific ribosomal protein genes (Rpl41 and Rps19) implicated in anti-inflammatory responses were present after BPC exposure. Increased IFNγ expression and other pro-neutrophilic mediator transcripts were increased in BPC-stimulated lung innate lymphoid cells. Further, the addition of BPC induced Hspa8 (encodes HSC70) and aryl hydrocarbon transcription factor activity across multiple lung ILC subsets. Overall, using an airway disease model that develops after occupational and environmental exposures, we demonstrate an effective method to better understand heterogenous ILC subset activation.

Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The results of the evaluation of the model’s performance show that it achieves a MAPE of 7.36, an RMSE of 27.91, a MAE of 18.43, and an R2 of 0.97. The verified model is applied to the power generation data of the selected inverters for the years 2020, 2021, and 2022. Through statistical analysis, it was determined that the error rate in 2022, the third year of its operation, increased by 159.55W on average from the error rate of the power generation forecast in 2020, the first year of operation. This indicates a 0.75% decrease in the inverter’s efficiency compared to the inverter’s power generation capacity. Therefore, it is judged that it can be applied effectively to analyses of inverter efficiency in the operation of photovoltaic plants.

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.

The construction industry is a challenging field for the application of robots. In particular, bridge construction, which involves many tasks at great heights, makes it difficult to implement robots. To construct a bridge, it is necessary to build numerous piers that can support the bridge deck. Pier construction involves a series of tasks including rebar connection, formwork installation, concrete pouring, formwork dismantling, and formwork reinstallation. These activities require working at heights, presenting a significant risk of falls. If bridge construction could be performed remotely using robots instead of relying on human labor, it would greatly contribute to the safety of bridge construction. This paper proposes a multi-robot system capable of remote operation and automation for rebar structure connection, concrete pouring, and concrete vibrating tasks in pier construction. The proposed multi-robot system for pier construction is composed of three robot systems. Each robot system consists of a robot arm mounted on a mobile robot that can move along rails. And to apply the proposed system to a construction site, it is essential to implement a compliance control algorithm that adapts to external forces. In this paper, we propose an admittance control that takes into account the weight of the tool for the compliance control of the proposed robot, which performs tasks by switching between various construction tools of different weights. Furthermore, we propose a synchronization control method for the multi-robot system to connect reinforcing structures. We validated the proposed algorithm through simulation. Furthermore, we developed a prototype of the proposed system to verify the feasibility of the suggested hardware design and control.

Recently, for the high performance of automobiles, the application ratio of zinc-coated steel sheets with excellent corrosion resistance has been increasing. However, it is difficult to achieve sound welds, as porosities form from zinc during welding. In this study, a laser welding process, with the addition of a pretreatment step to conventional laser welding, was devised to improve weld quality by eliminating pores caused by zinc in the welds. Laser welding was performed on the overlapping joint of hot-dip galvanized steel sheets (SGARC 340, Hyundai Steel., Suncheon-Si, Jeollanam-do, Republic of Korea), with a thickness of 1.2 mm in the upper sheet and 0.9 mm in the lower sheet, and the welding characteristics were investigated by varying the laser power and focal position. Compared to conventional laser welding, the three-step laser stitch welding process significantly reduced the degree of spatter generation and welding defects. Additionally, it increased the tensile–shear load by approximately 37%. Moreover, the reduction in the zinc component of the three-step laser stitch welds was confirmed through SEM-EDS analysis. These findings contribute valuable information for securing high-quality welding joints and effectively addressing zinc-coated steel sheet welding quality issues in the automotive and industrial sectors.

The objective of this study was to evaluate the microbiological properties of commercially available traditional Korean salted shrimp (Saeu-jeot) and to analyze the effects of saltwater immersion and ultraviolet (UV) irradiation on the reduction of Staphylococcus aureus in Saeu-jeot. A microbiological analysis was conducted across 56 commercially available Saeu-jeot samples from the Korean, Chinese, and Vietnamese markets. The microbiological analysis revealed no presence of Escherichia coli, coliforms, or Vibrio parahaemolyticus in any commercial samples. The total viable count and S. aureus were 3.8 ± 0.4 and 0.6 ± 0.3 log CFU/g, respectively. An investigation of the procedures conducted on Saeu-jeot samples at various production stages by Hazard Analysis Critical Control Point (HACCP)-certified companies demonstrated the substantial relevance of the raw material (tiny shrimp) on S. aureus counts. In order to reduce S. aureus in Saeu-jeot, saltwater immersion and UV irradiation treatments were applied, which reduced the S. aureus counts by 1.4 log CFU/g and 0.3 log CFU/g, respectively, and S. aureus’s efficacy was limited to the food’s surface. These results suggest that a co-treatment of saltwater immersion and UV irradiation could be effective in reducing S. aureus. The maintenance of hygienic handling and cleanliness are essential in the modern manufacturing processes of Saeu-jeot.

Abstract This study investigated the feature importance of near-infrared spectra from random forest regression models constructed to predict the carbonization characteristics of hydrochars produced by hydrothermal carbonization of kraft lignin. The model achieved high coefficients of determination of 0.989, 0.988, and 0.985 with root mean square errors of 0.254, 0.003, and 0.008 when predicting the carbon content, atomic O/C ratio, and H/C ratio, respectively. The random forest models outperformed the multilayer perceptron models for all predictions. In the feature importance analysis, the spectral regions at 1600–1800 nm, the first overtone of C–H stretching vibrations, and 2000–2300 nm, the combination bands, were highly important for predicting the carbon content and O/C predictions, whereas the region at 1250–1711 nm contributed to predicting H/C. The random forest models trained with the high-importance regions achieved better prediction performances than those trained with the entire spectral range, demonstrating the usefulness of the feature importance yielded by the random forest and the feasibility of selective application of the spectral data.

Saeu-jeot is a widely consumed variety of jeotgal, a South Korean salt-fermented food. However, there is a lack of existing studies conducting nontargeted metabolomic analyses of saeu-jeot during fermentation. To evaluate the changes in saeu-jeot during fermentation, saeu-jeot samples were fermented for 360 days under controlled conditions. Samples collected at different time points were subjected to physicochemical (including nontargeted metabolomic analysis) and microbial analyses. As fermentation progressed, the pH decreased and acidity increased, whereas total nitrogen, amino-nitrogen, and specific amino acid concentrations increased. Nontargeted metabolite analysis supports these results. Metabolite profiling classified changes in saeu-jeot during fermentation into those occurring in the early (15–45 days), middle (60–180 days), and late (270–360 days) stages. Pathogenic bacteria were not detected, and biogenic amine levels were not elevated, suggesting that saeu-jeot is safe to consume. Overall, pH, amino-nitrogen, and pathogenic bacteria, according to the fermentation stage of saeu-jeot, can be useful parameters for evaluating the quality of salted shrimp.

Doenjang and ganjang are traditional, Korean fermented foods. They are manufactured by fermenting jangs, either through the traditional natural fermentation, using straw, or the commercial inoculating starter cultures. However, both Bacillus cereus and aflatoxins have been detected in jangs, compromising their safety. Aflatoxins have been detected on numerous occasions. However, studies are yet to be conducted on whether these safety issues differ depending on the manufacturing method. In this study, we evaluated whether the manufacturing methods of doenjang and ganjang alter their safety. Samples of traditional and commercial doenjang and ganjang were analyzed for aflatoxin and B. cereus contamination. Microbiome taxonomic profiling was performed to assess microbial composition. The experimental methodology involved sample collection from various stages of production, including the use of starter cultures and natural fermentation processes. Aflatoxin levels were determined using regulatory limits, and B. cereus content was evaluated against specific thresholds. Aflatoxins were detected in both traditional and commercial doenjang and ganjang, with either the total aflatoxin (15 μg/kg) or aflatoxin B1 (10 μg/kg) exceeding the regulatory limits. However, ochratoxin A was not detected in any of the samples. B. cereus was detected in some samples, within the regulatory limit (4 log CFU/g), and was not influenced by the manufacturing method. Analysis at the production stage showed that aflatoxin increased alongside the fermentation time in traditional doenjang. However, in ganjang, no significant differences were associated with the fermentation period. When using starter cultures, the fermentation period did not affect the toxin level in both foods. Both methods showed lower aflatoxin content in the initial doenjang and ganjang samples than in meju. B. cereus was not detected in either method, as its content decreased over the fermentation period. Microbiome taxonomic profiling confirmed that even when using starter cultures, B. cereus was not a dominant species and was considerably affected by the environment. Staphylococcus aureus and Pseudomonas, pathogenic in nature, were detected in products manufactured using the traditional method; hence, the potential risk of this method was higher than that of the commercial method. The experimental methodology employed in this study contributes to understanding the microbial composition and toxin contamination levels in doenjang and ganjang, contributing to the overall knowledge of their safety and quality control.

A variety of mission equipment is mounted and utilized on UUVs (unmanned underwater vehicles) for the development of marine resources and underwater searching for military purposes. For exploration of a wide area, it is advantageous to use a towed underwater platform, and for precision exploration to a specific location, to use an active mobile ROV (remotely operated vehicles). Since the TUV (towed underwater vehicle) moves according to the speed of the towing vessel, it cannot be operated if the vessel is stationary or the speed is low. Therefore, TUVs do not have equipment that is useful at low speeds, such as optical cameras or forward looking sonars. If a TUV capable of active movement such as stationary or low-speed operation is developed, it can search a wide area and then accurately search for a specific location, with one platform mounting various mission equipment. In this paper, we propose a method for estimating the control model for a prototype of this novel TUV, and propose a depth and posture control algorithm to which the model is applied. The proposed TUV and control algorithm were verified by experiments under the flow rate environment in the circulating water channel.

Abstract The aim of this study is to establish prediction models for the non-destructive evaluation of the carbonization characteristics of lignin-derived hydrochars as a carbon material in real time. Hydrochars are produced via the hydrothermal carbonization of kraft lignins for 1–5 h in the temperature range of 175–250 °C, and as the reaction severity of hydrothermal carbonization increases, the hydrochar is converted to a more carbon-intensive structure. Principal component analysis using near-infrared spectra suggests that the spectral regions at 2132 and 2267 nm assigned to lignins and 1449 nm assigned to phenolic groups of lignins are informative bands that indicate the carbonization degree. Partial least squares regression models trained with near-infrared spectra accurately predicts the carbon content, oxygen/carbon, and hydrogen/carbon ratios with high coefficients of determination and low root mean square errors. The established models demonstrate better prediction than ordinary least squares regression models.

Photovoltaic (PV) technology converts solar energy into electrical energy, and the PV industry is an essential renewable energy industry. However, the amount of power generated through PV systems is closely related to unpredictable and uncontrollable environmental factors such as solar radiation, temperature, humidity, cloud cover, and wind speed. Particularly, changes in temperature and solar radiation can substantially affect power generation, causing a sudden surplus or reduction in the power output. Nevertheless, accurately predicting the energy produced by PV power generation systems is crucial. This paper proposes a hybrid model comprising a convolutional neural network (CNN) and long short-term memory (LSTM) for stable power generation forecasting. The CNN classifies weather conditions, while the LSTM learns power generation patterns based on the weather conditions. The proposed model was trained and tested using the PV power output data from a power plant in Busan, Korea. Quantitative and qualitative evaluations were performed to verify the performance of the model. The proposed model achieved a mean absolute percentage error of 4.58 on a sunny day and 7.06 on a cloudy day in the quantitative evaluation. The experimental results suggest that precise power generation forecasting is possible using the proposed model according to instantaneous changes in power generation patterns. Moreover, the proposed model can help optimize PV power plant operations.

One of the most important and challenging research subjects in computer vision is visual object tracking. The information obtained from the first frame consists of limited and insufficient information to represent an object. If prior information about robust representation that can represent an object well is not sufficient, object tracking fails when not robustly responding to changes in features of the target object according to various factors, namely shape, illumination variation, and scene distortion. In this paper, a real-time single object tracking algorithm is proposed based on a Siamese network to solve this problem. For the object feature extraction, we designed a fully convolutional neural network that removes a fully connected layer and configured a convolution block consisting of a bottleneck structure that preserves the information in a previous layer. This network was designed as a Siamese network, while a regional proposal network was combined at the end of the network for object tracking. The ImageNet Large-Scale Visual Recognition Challenge 2017 dataset was used to train the network in the pre-training phase. Then, in the experimental phase, the object tracking benchmark dataset was used to quantitatively evaluate the network. The experimental results revealed that the proposed tracking algorithm produced more competitive results compared to other tracking algorithms.

Owing to environmental constraints, it is challenging to stably conduct various missions or surveys of the seabed for a prolonged period in the marine environment. To address this challenge, several devices and technologies are being developed. In this study, we aimed to develop an unmanned underwater vehicle (UUV)—specifically, a towed underwater platform—that can be loaded and unloaded via joint operation with an unmanned surface vehicle, which can be connected to a wired cable to obtain a stable power supply and high-speed communication. In addition, various sensors for detection are employed to investigate the marine environment and conduct missions. Furthermore, we operated the developed UUV in actual waters, reviewed the results, and examined its practical operability.

We aimed to identify the optimum mixing ratio for various ingredients to obtain complex extracts with high extract yields and immune-enhancing activity in animals. The extract yield and amounts of nitric oxide (NO) and interleukin (IL)-6 were set to maximum for modeling predictions. The predicted optimum values for the mixing ratio were 49.5% for Rhodiola crenulata, 26.1% for Astragalus membranaceus, and 24.4% for Panax quinquefolius, and the predicted response values were 31.5% yield, 13.4% NO production, and 6.1% IL-6 production; actual values were 35.3% yield, 14.7% NO, and 6.6% IL-6. The optimum mixing ratio extract (OMRE) was used for the animal experiments. Treating mice with OMRE at 200 mg/kg produced significant increases in spleen indexes and T-cell/B-cell proliferation. OMRE treatment increased IL-10 and IL-6 production in concanavalin A- and lipopolysaccharide-induced T- and B- lymphocytes, respectively. These results provide a basis for the development of functional extracts and drinks.

For safe autonomous driving, deep neural network (DNN)-based perception systems play essential roles, where a vast amount of driving images should be manually collected and labeled with ground truth (GT) for training and validation purposes. After observing the manual GT generation’s high cost and unavoidable human errors, this study presents an open-source automatic GT generation tool, CarFree, based on the Carla autonomous driving simulator. By that, we aim to democratize the daunting task of (in particular) object detection dataset generation, which was only possible by big companies or institutes due to its high cost. CarFree comprises (i) a data extraction client that automatically collects relevant information from the Carla simulator’s server and (ii) a post-processing software that produces precise 2D bounding boxes of vehicles and pedestrians on the gathered driving images. Our evaluation results show that CarFree can generate a considerable amount of realistic driving images along with their GTs in a reasonable time. Moreover, using the synthesized training images with artificially made unusual weather and lighting conditions, which are difficult to obtain in real-world driving scenarios, CarFree significantly improves the object detection accuracy in the real world, particularly in the case of harsh environments. With CarFree, we expect its users to generate a variety of object detection datasets in hassle-free ways.

Liquid chromatography-tandem mass spectrometry (LC/MS/MS) is a more accurate technique for detecting proteins than electrophoresis-based methods such as western blotting. Because of its convenience, western blotting is commonly used for protein analysis in beef. We developed a method for detecting myosin light chain 3 (myl3) in beef samples, particularly dry-aged beef, using LC/MS/MS for quality testing. Musculus longissimus dorsi of Holstein was aged for 0, 2, 4, 5, 9, 11, 17, 20, and 24 weeks and used to measure the myl3 concentration. Because of the high molecular weight of myl3, the limitations of LC/MS/MS were overcome by implementing immunoprecipitation and digestion steps. Ultimately, a tryptic fragment of myl3 (13-mer), generated using immunoprecipitation and digestion by a biotinylated antibody, was detected using LC-MS/MS in positive ion mode through multiple reaction monitoring and analyte separation on a C18 column. Our method showed limits of detection and quantification of less than 0.3 and 0.8 μg/kg, respectively. However, differences in the myl3 concentrations according to the aging time were not significant (p > 0.05). After 12 weeks, myl3 disappeared in tested all samples, thus our analytical method can be used for accurate measurement of muscle protein in beef samples.

Abstract Beam–column joints in existing concrete buildings might not satisfy the design requirements for seismic reinforcement details specified in current seismic design codes. Thus, in this study, various retrofit solutions for existing exterior beam–column joints with non-seismic details were developed: head re-bars anchoring, carbon fiber reinforced polymer (CFRP) wrapping, haunch retrofit element, and steel jacketing with various shapes and sizes. To investigate the seismic performance of exterior joints strengthened with the proposed retrofit solutions, seven half-scale exterior reinforced concrete beam–column joints including one control specimen and six retrofitted specimens were fabricated and tested under cyclic loading simulating earthquake loading. The test results showed that the proposed retrofit solutions could partially enhance the seismic capacity of the beam–column joints: steel jackets could increase deformation and load-carrying capacities; steel haunch elements could increase the load-carrying capacity, stiffness, and dissipated energy; and head re-bar anchoring and CFRP wrapping did not significantly effect on the seismic capacity of the beam–column joints.

This study was conducted to investigate the microbiological, physicochemical, and visual quality of dried persimmons (Diospyros kaki Thunb. cv. Cheongdo-Bansi) during storage at various temperatures in order to determine the shelf-life. Two commercial dried persimmon samples were evaluated for changes in weight, moisture content, color, texture (hardness and gumminess), and microbial populations during storage at different temperatures (−20, 5, 12, and 25°C) for 70 days. Overall, dried persimmon-2 showed lower levels of total mesophilic bacteria, Escherichia coli, coliforms, yeasts, and molds than dried persimmon-1. Among the physicochemical qualities, significant differences were observed in color parameters such as L∗, a∗, and b∗ of the dried persimmons. However, no significant differences in weight, moisture content, and texture were observed in dried persimmons during storage for 70 days. Thus, changes in visual appearance and color index such as chroma value and browning index can be used as indicators for determining the shelf-life of dried persimmons.

Phenolic composition of green tea (Camellia sinensis) varies according to manufacturing processes that result in deglycosylation of glycosylated phenolics and condensation, epimerization, and degalloylation of flavan-3-ols (catechins). Ambiguous phenolic assignments based on UV absorbance alone can occur when the chromatographic peaks overlapped slightly. We established an improved method using an HPLC–UV coupled with a single-quadrupole MS detector (MS1) that can reject false UV peaks after checking the preceding MS1 peaks. Adjusted UV data coded by the Python algorithm were deployed to compare tea phenolics. Performance validation of the MS1 and UV analysis methods for 19 phenolics revealed a sensitivity of 0.17 and 0.47 pmol/injection, limit of detection of 15 and 33 μg/L, limit of quantification of 50 and 110 μg/L, intra-day precision of 5% and 1% relative standard deviation, and trueness of 83–135% and 97–100%, respectively. Our results suggest that the HPLC–UV–MS1 method, which is a low operational cost method, potentially provides the precise phenolic composition of teas.

Novel core-shell structure hard carbon/Si-carbon composites are prepared, and their electrochemical performances as an anode material for lithium-ion batteries are reported. Three different types of shell coating are applied using Si-carbon, Si-carbon black-carbon and Si-carbon black-carbon/graphite nanosheets. It appears that the use of n-Si/carbon black/carbon composite particles in place of n-Si for the shell coating is of great importance to achieve enhanced electrochemical performances from the core-shell composite samples, and additional wrapping with graphite nanosheets leads to a more stable cycle performance of the core-shell composites.

Eucalyptus pellita is known as attractive biomass, and it has been utilized for eucalyptus oil, furniture, and pulp and paper production that causes a significant amount of byproducts. Liquid hot water treatment depending on combined severity factor (CSF) was subjected to isolate hemicellulose fraction from E. pellita and to produce xylooligosaccharides (XOS). The xylan extraction ratio based on the initial xylan content of the feedstock was maximized up to 77.6% at 170 °C for 50 min condition (CSF: 1.0), which had accounted for XOS purity of 76.5% based on the total sugar content of the liquid hydrolysate. In this condition, the sum of xylobiose, xylotriose, and xylotetraose which has a low degree of polymerization (DP) of 2 to 4 was determined as 80.6% of the total XOS. The highest XOS production score established using parameters including the xylan extraction ratio, XOS purity, and low DP XOS ratio was 5.7 at CSF 1.0 condition. XOS production score evaluated using the CSF is expected to be used as a productivity indicator of XOS in the industry (R-squared value: 0.92).

When developing an automobile control application, its scheduling parameters as well as the control algorithm itself should be carefully optimized to achieve the best control performance from given computing resources. Moreover, since the wide acceptance of the AUTOSAR standard, where finer-granular scheduling entities (called runnables) rather than the traditional real-time tasks are used, the number of scheduling parameters to be optimized is far greater than the traditional task-based control systems. Hence, due to the vast problem space, it is not feasible to reuse existing time-consuming search-based optimization methods. With this motivation, this paper presents an analytical codesign method for deciding runnable periods that minimize given control cost functions. Our solution approach, based on the Lagrange multiplier method, can find optimized runnable periods in polynomial times due to its analytical nature. Moreover, our evaluation results for synthesized applications with varying complexities show that our method performs significantly better (12% to 59% of control cost reductions) than a state-of-the-art evolutionary algorithm. To the best of our knowledge, this study is one of the first attempts to find runnable periods that maximize a given system’s control performance.

Activation of signal transducer and activator of transcription 3 (STAT3) is well known to play a major role in the cell growth, survival, proliferation, metastasis, and angiogenesis of various cancer cells. Most of the citrus species offer large quantities of phytochemicals that have beneficial effects attributed to their chemical components. Our study was carried out to evaluate the anticancer effects of the pericarp of Iyokan ( Citrus iyo Hort. ex Tanaka), locally known as yeagam in Korea, through modulation of the STAT3 signaling pathway in both tumor cells and a nude mice model. The effect of supercritical extracts of yeagam peel (SEYG) on STAT3 activation, associated protein kinases, STAT3-regulated gene products, cellular proliferation, and apoptosis was examined. The in vivo effect of SEYG on the growth of DU145 human prostate xenograft tumors in athymic nu/nu male mice was also investigated. We found SEYG exerted substantial inhibitory effect on STAT3 activation in human prostate cancer DU145 cells as compared to other tumor cells analyzed. SEYG inhibited proliferation and downregulated the expression of various STAT3-regulated gene products such as bcl-2, bcl-xL, survivin, IAP-1/2, cyclin D1, cyclin E, COX-2, VEGF, and MMP-9. This correlated with an increase in apoptosis as indicated by an increase in the expression of p53 and p21 proteins, the sub-G1 arrest, and caspase-3-induced PARP cleavage. When administered intraperitoneally, SEYG reduced the growth of DU145 human prostate xenograft tumors through downmodulation of STAT3 activation in athymic nu/nu male mice. Overall, these results suggest that SEYG extract has the potential source of STAT3 inhibitors that may have a potential in chemoprevention of human prostate cancer cells.

Our objective was to improve the anti-inflammatory activity of guava leaf extract through optimization of extraction conditions by using response surface methodology. Central composite design was used to evaluate the effect of three independent variables – extraction temperature (X1), ethanol concentration (X2), and extraction time (X3) – on the response variables, i.e. LPS-induced prostaglandin E2 (PGE2) and NO production in RAW 264.7 cells. Analysis of variance showed that the predicted quadratic models were significant and suitable for the responses. From the superimposed four-dimensional response surface plots, the optimum ranges, which minimized PGE2 and NO production simultaneously, were X1 = 38–56 °C, X2 = 42–68%, and X3 = 3.2–6.6 h. When the extraction was performed at selected points within these optimal ranges, the experimental values were in close agreement with the predicted values. These results, when applied to industrial extraction processes, may help improve the anti-inflammatory effect of guava leaf extracts.

BackgroundValvular heart surgery (VHS) utilizing cardiopulmonary bypass (CPB) is inevitably associated with ischemic-reperfusion injury, which is known to depend on oxygen tension during reperfusion. The aim of this study was to evaluate the effect of arterial oxygen tension during reperfusion on myocardial recovery in patients undergoing VHS.MethodsFifty-six patients undergoing isolated VHS were randomly exposed to an oxygen fraction of 0.7 (hyperoxic group, n = 28) or 0.5 (normoxic group, n = 28) during reperfusion. All patients received an oxygen fraction of 0.7 during CPB. In the normoxic group, the oxygen fraction was lowered to 0.5 from the last warm cardioplegia administration to 1 minute after aortic unclamping, and was then raised back to 0.7. Hemodynamic data were measured after induction of anesthesia, weaning from CPB, and sternum closure. The frequency of cardiotonic medications used during and after weaning from CPB, and the short-term outcomes during the hospital stay were also assessed.ResultsThe frequency of vasopressin and milrinone use during weaning from CPB, but not norepinephrine, was significantly less in the normoxic group. The post-operative cardiac enzyme levels and short-term outcomes were not different between the groups.ConclusionsNormoxic reperfusion from the last cardioplegia administration to 1 minute after aortic unclamping in patients undergoing VHS resulted in significantly less frequent use of vasopressin and inotropics during weaning from CPB than hyperoxic reperfusion, although it did not affect the post-operative myocardial enzyme release or short-term prognosis.

Hemothorax is a possible immediate complication of central venous catheterization. We experienced a patient who suffered from massive hemothorax 72 hours after right subclavian venous catheterization. A 29-year-old female patient with Marfan's syndrome underwent the Bentall's operation and aortic arch replacement with an artificial graft, which was performed uneventfully. She recovered favorably in the intensive care unit and was transferred to the general ward on postoperative day 3. Immediately after the removal of the catheter in the general ward, massive hemothorax developed and emergent thoracotomy should have been performed to control bleeding. We report this case to re-emphasize the careful monitoring even after removal of central venous catheter and the need for ultrasound guidance during insertion of central venous catheters.

This study aimed to develop an advanced anomaly-detection system tailored for solar power distribution panels using thermal imaging cameras to ensure operational stability. It addresses the imperative shift toward digitalized safety management in electrical facilities, transcending the limitations of conventional empirical methodologies. Our proposed system leverages a faster R-CNN-based artificial intelligence model optimized through meticulous hyperparameter tuning to efficiently detect anomalies in distribution panels. Through comprehensive experimentation, we validated the efficacy of the system in accurately identifying anomalies, thereby propelling safety protocols forward during the fourth industrial revolution. This study signifies a significant stride toward fortifying the integrity and resilience of solar power distribution systems, which is pivotal for adapting to emerging technological paradigms and evolving safety standards in the energy sector. These findings offer valuable insights for enhancing the reliability and efficiency of safety management practices and fostering a safer and more sustainable energy landscape. [ABSTRACT FROM AUTHOR]

In recent years, the study of a single image super-resolution (SISR) is crucial to improving image resolution and using hardware technology to improve image resolution. SISR is widely used in satellite remote sensing, video surveillance, and medical image processing because it mainly relies on deep learning algorithms to realize the conversion from low-resolution (LR) images to high-resolution images. It has the advantages of low cost, simple operation, and high efficiency. This paper proposes an image super-resolution method based on a generative adversarial network named text localization generative adversarial nets (TLGAN) model. The method is improved based on superresolution generative adversarial networks (SRGAN), and the batch normalization layer is removed, which significantly reduces the computational burden of the model. In TLGAN model, we used the transfer learning method to pre-trained the model on the large dataset ImageNet, and then apply the pre-trained model to the cartoon image data set animes to achieve image super-resolution. Experimental results report that the proposed method has the advantages of fast running speed and excellent visual perception of super-resolution images compared with bicubic interpolation and SRGAN method. [ABSTRACT FROM AUTHOR]