GS Caltex (South Korea)

Generative artificial intelligence (GAI), a specialized branch of artificial intelligence, has developed as a dynamic discipline that drives innovation and creativity across several domains. It is concerned with creating models that can autonomously produce novel text, images, videos, music, 3D, code, and more. GAI is distinguished by its capacity to acquire knowledge from extensive datasets, identify recurring patterns, capture distributions, and produce novel content demonstrating similar features. This review presents a comprehensive historical overview of the development and progression of GAI techniques over the years. Various essential methodologies employed in developing GAI are also discussed, including Generative Adversarial Networks (GANs), Variational AutoEncoders (VAEs), transformers, and diffusion models. Moreover, a detailed overview of the technologies used in GAI is provided for generating images, videos, music, code, and text, such as ChatGPT, DALL-E, Midjourney, Claude, Bard, GitHub Copilot, and others. The research subsequently introduces the different datasets used to train the GAI models and the evaluation metrics for evaluating their performances. Ultimately, the research investigates the diverse applications of GAI across various domains, challenges, and ethical implications.

Recently, computer-aided design models and electromagnetic simulations have been used to augment synthetic aperture radar (SAR) data for deep learning. However, an automatic target recognition (ATR) model struggles with domain shift when using synthetic data because the model learns specific clutter patterns present in such data, which disturbs performance when applied to measured data with different clutter distributions. This study proposes IRASNet, a domain-generalized SAR-ATR framework designed to achieve effective feature-level clutter reduction and domain-invariant feature learning. The proposed framework introduces a clutter reduction module (CRM) that enhances the signal-to-clutter ratio on feature maps, mitigating the impact of clutter while preserving essential target and shadow information to improve ATR performance. To further enhance generalization, adversarial learning is integrated with CRM to extract clutter-reduced domain-invariant features, bridging the gap between synthetic and measured datasets without requiring measured data during training. Additionally, a positional supervision task is introduced using mask-based ground truth encoding to improve feature extraction from target and shadow regions, strengthening the model's class discrimination capability. Our proposed IRASNet not only enhances generalization performance but also significantly improves feature-level clutter reduction, making it a valuable advancement in the field of radar image pattern recognition.

This brief presents an I/Q imbalance calibration scheme for a 5G direct-conversion transmitter (TX). By utilizing a simple 1-bit phase-to-digital converter and 9-bit digital-to-analog converter, a quadrature phase error associated with fabrication tolerances can be minimized effectively using a binary search algorithm. This approach implements a continuous, real-time calibration that adaptively adjusts the resistive components of the type-I polyphase filter in response to detected phase errors, thereby ensuring precise phase alignment by dynamically compensating for I/Q imbalance without interrupting the primary signal path. The proposed idea is demonstrated in the LO path of a 5G direct-conversion transmitter in a 65-nm bulk CMOS technology. The measured image rejection ratio and LO feedthrough suppression ratio are maintained less than −52 dBc and −37 dBc, respectively, in the range of 27.5-29.5 GHz. The TX can support a peak data rate of 4.8 Gb/s at 28.5 GHz using 64-QAM and an error vector magnitude of −27.3 dB.

Extracellular vesicles (EVs) are crucial for intercellular communication and affect various physiological and pathological processes. Although terrestrial EVs have been extensively studied, marine-derived EVs have yet to be explored. This study investigated the therapeutic potential of sea cucumbers, known for their regenerative and immune abilities. Sea cucumber extracellular matrix (ECM)-anchored EVs (SEVs) were isolated and characterized using physical and electrophoretic analyses. Morphological assessments have shown that SEVs have shape and size distributions similar to mammalian EVs. Internal cargo analysis revealed the encapsulation of diverse proteins and genetic molecules. In anti-inflammatory tests with a lipopolysaccharide (LPS)-induced macrophage model, the results have shown that SEVs can alleviate inflammation factors regarding inducible nitric oxide synthase (iNOS) protein and immune-related mRNA expression. Microarray analysis was conducted to elucidate SEV's pharmacological efficacy and anti-inflammatory mechanisms, showing that SEVs inhibit the nucleotide-binding oligomerization domain (NOD)-like receptor (NLR) signaling pathway. An invivo study using a mouse model of atopic dermatitis (AD) induced by 2,4-dinitrochlorobenzene (DNCB) involved subcutaneous SEV administration, followed by severity scoring and histological analyses. Therapeutic efficacy analysis indicated improvements in the AD mouse models, including reduced skin thickness and mast cell numbers. These findings indicate their potential for treating AD. This study highlights the potential clinical applications of marine-derived EVs and offers important implications for future research and therapeutic developments.

Diphenyleneiodonium (DPI) has been widely used as an inhibitor of NADPH oxidase (Nox) to discover its function in cardiac myocytes under various stimuli. However, the effects of DPI itself on Ca2+ signaling and contraction in cardiac myocytes under control conditions have not been understood. We investigated the effects of DPI on contraction and Ca2+ signaling and their underlying mechanisms using video edge detection, confocal imaging, and whole-cell patch clamp technique in isolated rat cardiac myocytes. Application of DPI suppressed cell shortenings in a concentration-dependent manner (IC50 of ≅0.17 µM) with a maximal inhibition of ~70% at ~100 µM. DPI decreased the magnitude of Ca2+ transient and sarcoplasmic reticulum Ca2+ content by 20%–30% at 3 µM that is usually used to remove the Nox activity, with no effect on fractional release. There was no significant change in the half-decay time of Ca2+ transients by DPI. The L-type Ca2+ current (ICa) was decreased concentration-dependently by DPI (IC50 of ≅40.3 µM) with ≅13.1%-inhibition at 3 µM. The frequency of Ca2+ sparks was reduced by 3 µM DPI (by ~25%), which was resistant to a brief removal of external Ca2+ and Na+. Mitochondrial superoxide level was reduced by DPI at 3–100 µM. Our data suggest that DPI may suppress L-type Ca2+ channel and RyR, thereby attenuating Ca2+-induced Ca2+ release and contractility in cardiac myocytes, and that such DPI effects may be related to mitochondrial metabolic suppression.

This study proposes a method for optimizing satellite constellations and a series of process strategies to achieve the desired revisit performance for each target in the area of interest. Using a repeat ground track orbit of periodic characteristics, a tailored regionalsatellite-constellation method is developed to achieve the desired revisit performance for each target. The study consists of four parts representing four programs: finding orbit elements through optimization techniques; producing an access matrix between targets and satellites; deriving the number of satellites for each target for the desired revisit performance; and deploying satellite groups by minimizing the orbital plane. Then, the superiority of this optimization method is verified through numerical comparisons and contour distributions for revisit performance compared to the existing conventional Walker method.

Significant challenges are involved in tactical flying ad-hoc network (FANET) missions because network environments are very dynamic. In addition, energy-efficient network operation is important in tactical FANETs owing to the limited capacity of the on-board battery in unmanned aerial vehicles (UAVs). In a slotted-ALOHA (S-ALOHA)-based tactical FANET, frequent packet collisions due to changes in the network environment deteriorate the energy efficiency. Therefore, accurately estimating the number of active UAVs is crucial for improving the performance of S-ALOHA-based networks. Several estimation methods such as low-bound, Schoute, max-probability, and Bayesian estimation have been studied, and these methods perform well in static network environments; however, the estimation error significantly increases in dynamic network environments. To accurately estimate the number of active UAVs in highly dynamic environments, this study proposes an online hybrid pseudo-Bayesian estimation (HyPE) method. Specifically, this method combines the pure-Bayesian and pseudo-Bayesian estimation methods to overcome their shortages such as the inability in a dynamic environment of the pure-Bayesian method and the low estimation accuracy of the pseudo-Bayesian method. This paper compares the performance of the proposed HyPE method with that of benchmark methods in terms of the estimation error according to the variation period and variation step size. The results show that HyPE is more adaptable to dynamic changes in network environments. Bayesian estimation, unmanned aerial vehicle (UAV), active UAV, slotted-ALOHA, tactical flying ad-hoc network (FANET). JIMIN JEON (Student Member, IEEE) received the B.S. degree from the

Recently, multi-hop flying ad hoc networks (FANETs) have received considerable attention because of their various advantages, such as ease of deployment, scalability, cost, and latency reduction. However, FANETs still encounter serious problems such as a highly dynamic network topology and the limited on-board battery of unmanned aerial vehicles (UAVs). Thus, to increase the coverage probability while reducing unnecessary packet transmissions, realizing efficient and effective relay selection is important. To resolve these issues, this paper proposes a proactive and probabilistic uncovered neighbor-aware relay selection (P 2 URE) method that considers FANET's topological dynamics. Specifically, the relay decision factor to identify relay UAV candidates and the probabilistic relay decision to reduce coverage overlaps and minimize redundant packet transmissions are devised. Through extensive simulations, it has been validated that the proposed P 2 URE method surpasses the benchmark methods in terms of the total number of transmissions, coverage probability, and energy efficiency. This superiority is observed across various network dynamics resulting from variations in the UAV density, speed, neighbor-information update period, and delay interval. INDEX TERMS Uncovered neighbor, neighbor information update, aerial relay selection, relay decision factor, unmanned aerial vehicle, flying ad hoc network.