• Journal of Veterinary Science
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• v.23 no.5
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• pp.74.1-74.16
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• 2022

Background: Previous studies have presented evidence to support the significant association between red meat intake and colon cancer, suggesting that heme iron plays a key role in colon carcinogenesis. Epigallocatechin-3-gallate (EGCG), the major constituent of green tea, exhibits anti-oxidative and anti-cancer effects. However, the effect of EGCG on red meat-associated colon carcinogenesis is not well understood. Objectives: We aimed to investigate the regulatory effects of hemin and EGCG on colon carcinogenesis and the underlying mechanism of action. Methods: Hemin and EGCG were treated in Caco2 cells to perform the water-soluble tetrazolium salt-1 assay, lactate dehydrogenase release assay, reactive oxygen species (ROS) detection assay, real-time quantitative polymerase chain reaction and western blot. We investigated the regulatory effects of hemin and EGCG on an azoxymethane (AOM) and dextran sodium sulfate (DSS)-induced colon carcinogenesis mouse model. Results: In Caco2 cells, hemin increased cell proliferation and the expression of cell cycle regulatory proteins, and ROS levels. EGCG suppressed hemin-induced cell proliferation and cell cycle regulatory protein expression as well as mitochondrial ROS accumulation. Hemin increased nuclear factor erythroid-2-related factor 2 (Nrf2) expression, but decreased Keap1 expression. EGCG enhanced hemin-induced Nrf2 and antioxidant gene expression. Nrf2 inhibitor reversed EGCG reduced cell proliferation and cell cycle regulatory protein expression. In AOM/DSS mice, hemin treatment induced hyperplastic changes in colon tissues, inhibited by EGCG supplementation. EGCG reduced the hemin-induced numbers of total aberrant crypts and malondialdehyde concentration in the AOM/DSS model. Conclusions: We demonstrated that EGCG reduced hemin-induced proliferation and colon carcinogenesis through Nrf2-inhibited mitochondrial ROS accumulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• Korean Journal of Ecology and Environment
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• v.56 no.4
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• pp.339-347
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• 2023

Dissolved organic matter (DOM) is a key component in the biogeochemical cycling in freshwater ecosystem. However, it has been rarely explored, particularly complex river watershed dominated by natural and anthropogenic sources, such as various effluent facility and livestock. The current research developed a new analytical method for TOC/TN (Total Organic Carbon/Total Nitrogen) stable isotope ratio, and distinguish DOM source using stable isotope value (δ¹³C-DOC) and spectroscopic indices (fluorescence index [FI] and biological index [BIX]). The TOC/TN-IR/MS analytical system was optimized and precision and accuracy were secured using two international standards (IAEA-600 Caffein, IAEA-CH-6 Sucrose). As a result of controlling the instrumental conditions to enable TOC stable isotope analysis even in low-concentration environmental samples (<1 mgC L^-1), the minimum detection limit was improved. The 12 potential DOM source were collected from watershed, which includes top-soils, groundwater, plant group (fallen leaves, riparian plants, suspended algae) and effluent group (pig and cow livestock, agricultural land, urban, industry facility, swine facility and wastewater treatment facilities). As a result of comparing characteristics between 12 sources using spectroscopic indices and δ¹³C-DOC values, it were divided into four groups according to their characteristics as a respective DOM sources. The current study established the TOC/TN stable isotope analyses system for the first time in Korea, and found that spectroscopic indices and δ¹³C-DOC are very useful tool to trace the origin of organic matter in the aquatic environments through library database.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.964-970
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• 2023

As part of the International Maritime Organization ef orts to reduce greenhouse gas emissions, the maritime industry is exploring low-carbon fuels such as liquefied natural gas and methanol, as well as zero-carbon fuels such as hydrogen and ammonia, evaluating them as environmentally friendly alternatives. Particularly, ammonia has substantial operational experience as cargo on transport ships, and ammonia ship engines are expected to be available in the second half of 2024, making it relatively accessible for commercial use. However, overcoming the toxicity challenges associated with using ammonia as a fuel is imperative. Detection is possible at levels as low as 5 ppm through olfactory senses, and exposure to concentrations exceeding 300 ppm for more than 30 min can result in irreparable harm. Using the KORA program provided by the Chemical Safety Agency, an assessment of the potential risks arising from leaks during ammonia bunkering was conducted. A 1-min leak could lead to a 5 ppm impact within a radius of approximately 7.5 km, affecting key areas in Busan, a major city. Furthermore, the potentially lethal concentration of 300 ppm could have severe consequences in densely populated areas and schools near the bunkering site. Therefore, given the absence of regulations related to ammonia bunkering, the potential for widespread toxicity from even minor leaks highlights the requirement for the development of legislation. Establishing an integrated system involving local governments, fire departments, and environmental agencies is crucial for addressing the potential impacts and ensuring the safety of ammonia bunkering operations.

• Information Systems Review
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• v.25 no.4
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• pp.47-65
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• 2023

In this paper, we investigate how the online voting system can be a trust-based system from a technical perspective. Under four principles of voting, we finely evaluate the existing belief that offline voting is safer and more reliable than online voting based on procedural processes, technical principles. Many studies have suggested the ideas for implementing online voting system, but they have not attempted to strictly examine the technologies of online voting system from the perspective of voting requirements, and usually verification has been insufficient in terms of practical acceptance. Therefore, this study aims to analyze how the technologies are utilized to meet the demanding requirements of voting based on the technologies proven in the field. In addition to general data encryption, online voting requires more technologies for preventing data manipulation and verifying voting results. Moreover, high degree of confidentiality is required because voting data should not be exposed not only to outsiders but also to managers or the system itself. To this end, the security techniques such as Blind Signature, Bit Delegation and Key Division are used. In the case of blockchain-based voting, Mixnet and Zero-Knowledge Proof are required to ensure anonymity. In this study, the current status of the online voting system is analyzed based on the field system that actually serves. This study will enhance our understanding on online voting security technologies and contribute to build a more trust-based voting mechanism.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.410-418
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• 2023

Recently, advancements and commercialization in the field of maritime autonomous surface ships (MASS) has rapidly progressed. Concurrently, studies are also underway to develop methods for automatically surveying the condition of various on-board equipment remotely to ensure the navigational safety of MASS. One key issue that has gained prominence is the method to obtain values from analog gauges installed in various equipment through image processing. This approach has the advantage of enabling the non-contact detection of gauge values without modifying or changing already installed or planned equipment, eliminating the need for type approval changes from shipping classifications. The objective of this study was to identify a dynamically changing indicator needle within noisy images of analog gauges. The needle object must be identified because its position significantly affects the accurate reading of gauge values. An analog pressure gauge attached to an emergency fire pump model was used for image capture to identify the needle object. The acquired images were pre-processed through Gaussian filtering, thresholding, and morphological operations. The needle object was then identified through Hough Transform. The experimental results confirmed that the center and object of the indicator needle could be identified in images of noisy analog gauges. The findings suggest that the image processing method applied in this study can be utilized for shape identification in analog gauges installed on ships. This study is expected to be applicable as an image processing method for the automatic remote survey of MASS.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.403-409
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• 2024

In this paper, the guidelines for the design of an Artificial Intelligence(AI) based Integrated Process Safety Management(PSM) system to enhance workplace safety using data from process safety reports submitted by hazardous and risky facility operators in accordance with the Occupational Safety and Health Act is proposed. The system composed of the proposed guidelines is to be implemented separately by individual facility operators and specialized process safety management agencies for single or multiple workplaces. It is structured with key components and stages, including data collection and preprocessing, expansion and segmentation, labeling, and the construction of training datasets. It enables the collection of process operation data and change approval data from various processes, allowing potential fault prediction and maintenance planning through the analysis of all data generated in workplace operations, thereby supporting decision-making during process operation. Moreover, it offers utility and effectiveness in time and cost savings, detection and prediction of various risk factors, including human errors, and continuous model improvement through the use of accurate and reliable training data and specialized datasets. Through this approach, it becomes possible to enhance workplace safety and prevent accidents.

• The Journal of Bigdata
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• v.9 no.1
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• pp.75-88
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• 2024

Recently, there have been cases reported in the news of individuals experiencing symptoms of food poisoning after consuming raw beef purchased from online platforms, or reviews claiming that cherry tomatoes tasted bitter. This suggests the potential for analyzing food reviews on online platforms to detect food hazards, enabling government agencies, food manufacturers, and distributors to manage consumer food safety risks. This study proposes a classification model that uses sentiment analysis and large language models to analyze food reviews and detect negative ones, multi-labeling key food safety hazards (food poisoning, spoilage, chemical odors, foreign objects). The sentiment analysis model effectively minimized the misclassification of negative reviews with a low False Positive rate using a 'funnel' model. The multi-labeling model for food safety hazards showed high performance with both recall and accuracy over 96% when using GPT-4 Turbo compared to GPT-3.5. Government agencies, food manufacturers, and distributors can use the proposed model to monitor consumer reviews in real-time, detect potential food safety issues early, and manage risks. Such a system can protect corporate brand reputation, enhance consumer protection, and ultimately improve consumer health and safety.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.404-412
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• 2024

The adoption of cloud environments has revolutionized application deployment and management, with microservices architecture and container technology serving as key enablers of this transformation. However, these advancements have introduced new challenges, particularly the necessity to precisely understand service interactions and conduct detailed analyses of internal processes within complex service environments such as microservices. Traditional monitoring techniques have proven inadequate in effectively analyzing these complex environments, leading to increased interest in eBPF (extended Berkeley Packet Filter) technology as a solution. eBPF is a powerful tool capable of real-time event collection and analysis within the Linux kernel, enabling the monitoring of various events, including file system activities within the kernel space. This paper proposes a container activity analysis system based on eBPF, which monitors events occurring in the kernel space of both containers and host systems in real-time and analyzes the collected data. Furthermore, this paper conducts a comparative analysis of prominent eBPF-based container monitoring systems (Tetragon, Falco, and Tracee), focusing on aspects such as event detection methods, default policy application, event type identification, and system call blocking and alert generation. Through this evaluation, the paper identifies the strengths and weaknesses of each system and determines the necessary features for effective container process monitoring and restriction. In addition, the proposed system is evaluated in terms of container metadata collection, internal activity monitoring, and system metadata integration, and the effectiveness and future potential of eBPF-based monitoring systems.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1286-1288
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• 2024

The construction industry, known for its inherent risks and multiple hazards, necessitates effective solutions for hazard identification and mitigation [1]. To address this need, the implementation of machine learning models specializing in object detection has become increasingly important because this technological approach plays a crucial role in augmenting worker safety by proactively recognizing potential dangers on construction sites [2], [3]. However, the challenge in training these models lies in obtaining accurately labeled datasets, as conventional methods require labor-intensive labeling or costly measurements [4]. To circumvent these challenges, synthetic data generation (SDG) has emerged as a key method for creating realistic and diverse training scenarios [5], [6]. The paper reviews the evolution of synthetic data generation tools, highlighting the shift from earlier solutions like Synthpop and Data Synthesizer to advanced game engines[7]. Among the various gaming platforms, Unity 3D and Unreal Engine stand out due to their advanced capabilities in replicating realistic construction hazard environments [8], [9]. Comparing Unity 3D and Unreal Engine is crucial for evaluating their effectiveness in SDG, aiding developers in selecting the appropriate platform for their needs. For this purpose, this paper conducts a comparative analysis of both engines assessing their ability to create high-fidelity interactive environments. To thoroughly evaluate the suitability of these engines for generating synthetic data in construction site simulations, the focus relies on graphical realism, developer-friendliness, and user interaction capabilities. This evaluation considers these key aspects as they are essential for replicating realistic construction sites, ensuring both high visual fidelity and ease of use for developers. Firstly, graphical realism is crucial for training ML models to recognize the nuanced nature of construction environments. In this aspect, Unreal Engine stands out with its superior graphics quality compared to Unity 3D which typically considered to have less graphical prowess [10]. Secondly, developer-friendliness is vital for those generating synthetic data. Research indicates that Unity 3D is praised for its user-friendly interface and the use of C# scripting, which is widely used in educational settings, making it a popular choice for those new to game development or synthetic data generation. Whereas Unreal Engine, while offering powerful capabilities in terms of realistic graphics, is often viewed as more complex due to its use of C++ scripting and the blueprint system. While the blueprint system is a visual scripting tool that does not require traditional coding, it can be intricate and may present a steeper learning curve, especially for those without prior experience in game development [11]. Lastly, regarding user interaction capabilities, Unity 3D is known for its intuitive interface and versatility, particularly in VR/AR development for various skill levels. In contrast, Unreal Engine, with its advanced graphics and blueprint scripting, is better suited for creating high-end, immersive experiences [12]. Based on current insights, this comparative analysis underscores the user-friendly interface and adaptability of Unity 3D, featuring a built-in perception package that facilitates automatic labeling for SDG [13]. This functionality enhances accessibility and simplifies the SDG process for users. Conversely, Unreal Engine is distinguished by its advanced graphics and realistic rendering capabilities. It offers plugins like EasySynth (which does not provide automatic labeling) and NDDS for SDG [14], [15]. The development complexity associated with Unreal Engine presents challenges for novice users, whereas the more approachable platform of Unity 3D is advantageous for beginners. This research provides an in-depth review of the latest advancements in SDG, shedding light on potential future research and development directions. The study concludes that the integration of such game engines in ML model training markedly enhances hazard recognition and decision-making skills among construction professionals, thereby significantly advancing data acquisition for machine learning in construction safety monitoring.