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With rising global obesity rates, the demand for effective dietary strategies for obesity management has intensified. This study evaluated the potential of kimchi with various probiotics and bioactive compounds as a dietary intervention for high-fat diet (HFD)-induced obesity in rats. Through a comprehensive analysis incorporating global and targeted metabolomics, gut microbiota profiling, and biochemical markers, we investigated the effects of the 12-week kimchi intake on HFD-induced obesity. Kimchi intake modestly mitigated HFD-induced weight gain and remarkably altered gut microbiota composition, steroid hormones, bile acids, and metabolic profiles, but did not reduce adipose tissue accumulation. It also caused significant shifts in metabolomic pathways, including steroid hormone metabolism, and we found substantial interactions between dietary interventions and gut microbiota composition. Although more research is required to fully understand the anti-obesity effects of kimchi, our findings support the beneficial role of kimchi in managing obesity and related metabolic disorders. [ABSTRACT FROM AUTHOR]

Highlights Used one- and two-point normalization to compensate for drifts in EC and pH probes in hydroponic systems. Developed automated sensor drift compensation systems for hydroponic systems. Evaluated the compensation systems for open- and closed-hydroponics. Application of the normalization methods increased the accuracy of the EC and pH measurements. In hydroponic systems, the nutrient solutions are dispensed based on pH and electrical conductivity (EC) to ensure that the nutrient components in the solution are optimal for crop growth. However, the pH and EC probes may exhibit signal drifts when immersed in the solution for a considerable period, which deteriorate the measurement accuracy and the nutrient and water use efficiency. Notably, the existing hydroponic systems require manual calibration of the pH and EC probes and do not provide any information regarding their accuracy. Consequently, faults in the probes are challenging to detect. In this study, two types of automated drift compensation systems for EC and pH probes, based on one- and two-point normalization, were developed to accurately monitor changes in the pH and EC values in hydroponic solutions and enhance sensor management. The proposed framework can provide information regarding the drifts of the pH and EC probes through automated normalization solution measurements. The effectiveness of the developed system in scenarios involving plant growth with open and closed hydroponics was evaluated through comparison with a standard method involving sampling and laboratory analysis. The results indicated that the one-point normalization strategy had a simple structure and was effective in compensating drifted offsets of sensors. A two-point normalization strategy could effectively predict the sensor drifts as well as variations in the sensitivities, which could be used in the compensation processes. Application of the one-point normalization-based system increased the accuracy of the EC and pH measurements, with root mean square errors decreasing from 50 to 28.5 µS·cm-1 and 0.43 to 0.17 pH in the open hydroponic systems and from 68.7 to 38.3 µS·cm-1 and 0.15 to 0.11 pH in the closed systems, respectively. The two-point normalization-based system decreased the RMSEs from 44.6 to 33.4 µS·cm-1 and 0.55 to 0.20 pH for the EC and pH measurements, respectively. In addition, the decreased slope and coefficient of determination in the linear regression according to the system application proved the system can compensate for the drifts of the EC and pH probes. The proposed system provided accurate EC and pH measurements and sensor status information, promoting more efficient nutrient use and sensor maintenance. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of cybersecurity, the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly critical. Traditional methods often fall short in capturing the dynamic and complex nature of modern cyber threats. To address this gap, we propose a comprehensive framework designed to create authentic network environments tailored for cybersecurity exercise systems. Our framework leverages advanced simulation techniques to generate scenarios that mirror actual network conditions faced by professionals in the field. The cornerstone of our approach is the use of a conditional tabular generative adversarial network (CTGAN), a sophisticated tool that synthesizes realistic synthetic network traffic by learning from real data patterns. This technology allows us to handle technical components and sensitive information with high fidelity, ensuring that the synthetic data maintains statistical characteristics similar to those observed in real network environments. By meticulously analyzing the data collected from various network layers and translating these into structured tabular formats, our framework can generate network traffic that closely resembles that found in actual scenarios. An integral part of our process involves deploying this synthetic data within a simulated network environment, structured on software-defined networking (SDN) principles, to test and refine the traffic patterns. This simulation not only facilitates a direct comparison between the synthetic and real traffic but also enables us to identify discrepancies and refine the accuracy of our simulations. Our initial findings indicate an error rate of approximately 29.28% between the synthetic and real traffic data, highlighting areas for further improvement and adjustment. By providing a diverse array of network scenarios through our framework, we aim to enhance the exercise systems used by cybersecurity professionals. This not only improves their ability to respond to actual cyber threats but also ensures that the exercise is cost-effective and efficient. [ABSTRACT FROM AUTHOR]