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Considering the nature of marine accidents, even a single accident can result in significant damage to the environment and property, as well as loss of life. Therefore, the initial response should be rapid and accurate, and various decision support systems have been developed to achieve this. Research on simulating progressive flooding on board immediately after an accident is being actively conducted, but this requires high levels of computing power. In this study, a methodology for converting simulated ship motion data into a ship motion database is presented. The model of a training ship from the Korea Institute of Maritime and Fisheries Technology and KRISO in-house code SMTP was used for ship motion computations. The short-time Fourier transform was used to convert time-series motion data into a spectrogram motion database. A methodology for deriving a predicted location of the damage center is presented. The candidate locations of the damage centers were obtained by comparing the root mean square error values of the ship motion database from the simulation and real-time ship motion data. Finally, a probability function was suggested to confirm the predicted location of the damage center. Using 100 randomly selected test cases, our method showed 95% accuracy.

We designed a high-sensitivity magnetic resonance imaging contrast agent that could be used to diagnose diseases. First, magnetic nanocrystals were synthesized by a thermal decomposition method on an organic solvent to obtain a high magnetism and methoxy poly(ethylene glycol)-poly(lactic acid) as an amphiphilic polymer using the ring-opening polymerization method to stably disperse the magnetic nanocrystals in an aqueous phase. Subsequently, the magnetic nanoclusters simultaneously self-assembled with methoxy poly(ethylene glycol)-poly(lactic acid) using the nano-emulsion method to form magnetic nanoclusters. Because their shape was similar to a raspberry, they were named PEGylated magnetic nano-assemblies. The PEGylated magnetic nano-assemblies were dispersed stably in the aqueous phase with a uniform size of approximately 65–70 nm for an extended period (0 days: 68.8 ± 5.1 nm, 33 days: 69.2 ± 2.0 nm, and 44 days: 63.2 ± 5.6). They exhibited both enough of a magnetic resonance (MR) contrast effect and biocompatibility. In an in vivo study, the PEGylated magnetic nano-assemblies provided a high contrast effect for magnetic resonance images for a long time after one treatment, thereby improving the diagnostic visibility of the disease site.

Cyber-attacks have become a significant concern today, particularly in IoT environments where security poses a substantial challenge due to the distributed nature and heterogeneity of protocols. To efficiently detect threats in IoT networks, it is crucial to develop a robust intrusion-detection system (IDS) capable of identifying various modern and traditional attacks with high accuracy. Most existing machine-learning-based intrusion-detection systems for IoT have been trained using outdated datasets that do not accurately reflect IoT scenarios. Additionally, current research does not adequately address which machine-learning classifiers are most suitable for developing an efficient IDS in IoT environments. In our research, we have developed and trained a real-time intrusion-detection system for IoT networks that can detect multiple modern and traditional threats with high accuracy. We created seven instances of real-time IDS using state-of-the-art machine-learning algorithms, including Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Naïve Bayes, and Artificial Neural Networks. Using the Pearson Correlation Coefficient, we extracted the most relevant features from the BoT–IoT dataset. After rigorous preprocessing, we used these data to train our algorithms. Our trained model, INIDS, is not only up to date and real-time but also capable of accurately identifying multiple categories of attacks specifically related to IoT networks. To achieve maximum accuracy, instead of selecting only one classifier, we evaluated seven advanced machine-learning algorithms and provided a comprehensive comparison of their performance and efficiency in the context of IoT networks. This analysis can guide future researchers in choosing the right machine-learning algorithms for developing IDS. We found that Random Forest is the most robust classifier for IoT-based network intrusion-detection systems, achieving an accuracy of 99.2%. The second-best performer is Naïve Bayes, with an accuracy of 98.8%.

The escalating global demand for nutritious and sustainable food sources has heightened interest in microbial biotransformation in food, a process in which microorganisms chemically modify food components to enhance their functional properties and nutritional content. This review provides a comprehensive overview of recent advancements in microbial fermentation, focusing on its impact on food quality, nutritional enhancement, and public health. Emphasizing both traditional fermentation practices and modern biotechnological innovations, this review highlights how beneficial microorganisms transform raw food substrates into products with superior nutrient bioavailability and functional benefits. Key developments include the synthesis of bioactive compounds, reduction of anti-nutritional factors, and improvement of sensory attributes, all of which collectively contribute to disease prevention, gut health, and overall well-being. Additionally, this review addresses safety concerns and challenges in microbial fermentation, including microbiological hazards, chemical contaminants, and strategies for their management. It also explores the difficulties in scaling up these processes for industrial applications and discusses future directions for sustainable food production. By consolidating current knowledge and identifying emerging trends, this review provides a comprehensive resource for researchers, industry professionals, and policymakers, offering insights into leveraging microbial biotransformation to create nutritionally enriched and functionally enhanced food products.

This study investigates the integration of quantum computing, classical methods, and deep learning techniques for enhanced image processing in dynamic 6G networks, while also addressing essential aspects of copyright technology and detection. Our findings indicate that quantum methods excel in rapid edge detection and feature extraction but encounter difficulties in maintaining image quality compared to classical approaches. In contrast, classical methods preserve higher image fidelity but struggle to satisfy the real-time processing requirements of 6G applications. Deep learning techniques, particularly CNNs, demonstrate potential in complex image analysis tasks but demand substantial computational resources. To promote the ethical use of AI-generated images, we introduce copyright detection mechanisms that employ advanced algorithms to identify potential infringements in generated content. This integration improves adherence to intellectual property rights and legal standards, supporting the responsible implementation of image processing technologies. We suggest that the future of image processing in 6G networks resides in hybrid systems that effectively utilize the strengths of each approach while incorporating robust copyright detection capabilities. These insights contribute to the development of efficient, high-performance image processing systems in next-generation networks, highlighting the promise of integrated quantum-classical–classical deep learning architectures within 6G environments.

Although fucoidan has been shown to exert anticancer activity against several types of cancer cell lines, no reports have explored fucoidan-affected cell growth in human urinary bladder cancer cells. In this study, we investigated the anti-proliferative effects of fucoidan in human bladder cancer T24 cells. Our results indicated that fucoidan decreased the viability of T24 cells through the induction of G1 arrest and apoptosis. Fucoidan-induced G1 arrest is associated with the enhanced expression of the Cdk inhibitor p21WAF1/CIP1 and dephosphorylation of the pRB along with enhanced binding of p21 to Cdk4/6 as well as pRB to the transcription factor E2Fs. Further investigations showed the loss of mitochondrial membrane potential and the release of cytochrome c from mitochondria to cytosol, proving mitochondrial dysfunction upon fucoidan treatment with a corresponding increase in the Bax/Bcl-2 expression ratio. Fucoidan-triggered apoptosis was also accompanied by the up-regulation of Fas and truncated Bid as well as the sequential activation of caspase-8. Furthermore, a significant increased activation of caspase-9/-3 was detected in response to fucoidan treatment with the decreased expression of IAPs and degradation of PARP, whereas a pan-caspase inhibitor significantly suppressed apoptosis and rescued the cell viability reduction. In conclusion, these observations suggest that fucoidan attenuates G1-S phase cell cycle progression and serves as an important mediator of crosstalk between caspase-dependent intrinsic and extrinsic apoptotic pathways in T24 cells.

Identifying the cause of vehicle noise is a basic requirement for the development of low-noise vehicles. The tire pattern noise depends on the tire itself and causes complex and unpredictable sounds. In pneumatic tire pattern design, the prediction technology of the tire pattern noise according to pattern shape design is important. The conventional method of predicting tire pattern noise is to simply scan the pattern shape of tire and to analyze its spectrum. However, this method has limitations because it does not consider the transfer function and precise mechanism of tire pattern noise. In this study, adaptive filter theory was applied to identify the transfer function between the grooves of patterns and measured acoustic data. To predict the waveform of an actual pattern noise in the time domain, the impulse response of this transfer function was convolved by the scanned pattern input of tires. The predicted waveform of pattern noise was validated with the waveforms of measured noise data. Finally, a sound quality index (SQI) of tire pattern noise was developed using the measured pattern noises and was applied to estimate the sound quality of pattern noise. Eventually, using the prediction method from this study, we hope to reduce the time and cost spent on tire pattern design and verification.

Corydaline is a bioactive alkaloid with various antiacetylcholinesterase, antiallergic, and antinociceptive activities found in the medicinal herb Corydalis Tubers. The inhibitory potential of corydaline on the activities of seven major human cytochrome P450 and four UDP-glucuronosyltransferase enzymes in human liver microsomes was investigated using LC-tandem MS. Corydaline was found to inhibit CYP2C19-catalyzed S-mephenytoin-4’-hydroxylatoin and CYP2C9-catalyzed diclofenac 4-hydroxylation, with Ki values of 1.7 and 7.0 mM, respectively. Corydaline also demonstrated moderate inhibition of UGT1A1-mediated 17b-estradiol 3-glucuronidation and UGT1A9-mediated propofol glucuronidation with Ki values of 57.6 and 37.3 mM, respectively. In the presence of corydaline, CYP3A-mediated midazolam hydroxylation showed a decrease with increasing preincubation time in a dose-dependent manner with Ki values of 30.0 mM. These in vitro results suggest that corydaline should be evaluated for potential pharmacokinetic drug interactions in vivo due to potent inhibition of CYP2C19 and CYP2C9.

Eupatilin and jaceosidin are bioactive flavones found in the medicinal herbs of the genus Artemisia. These bioactive flavones exhibit various antioxidant, antiinflammatory, antiallergic, and antitumor activities. The inhibitory potentials of eupatilin and jaceosidin on the activities of seven major human cytochrome P450 enzymes in human liver microsomes were investigated using a cocktail probe assay. Eupatilin and jaceosidin potently inhibited CYP1A2-catalyzed phenacetin O-deethylation with 50% inhibitory concentration (IC50) values of 9.4 mM and 5.3 mM, respectively, and CYP2C9-catalyzed diclofenac 4-hydroxylation with IC50 values of 4.1 mM and 10.2 mM, respectively. Eupatilin and jaceosidin were also found to moderately inhibit CYP2C19-catalyzed [S]-mephenytoin 4¢-hydroxylation, CYP2D6-catalyzed bufuralol 1¢-hydroxylation, and CYP2C8-catalyzed amodiaquine N-deethylation. Kinetic analysis of human liver microsomes showed that eupatilin is a competitive inhibitor of CYP1A2 with a Ki value of 2.3 mM and a mixed-type inhibitor of CYP2C9 with a Ki value of 1.6 mM. Jaceosidin was shown to be a competitive inhibitor of CYP1A2 with a Ki value of 3.8 mM and a mixed-type inhibitor of CYP2C9 with Ki value of 6.4 mM in human liver microsomes. These in vitro results suggest that eupatilin and jaceosidin should be further examined for potential pharmacokinetic drug interactions in vivo due to inhibition of CYP1A2 and CYP2C9.