Korea Nazarene University

Cytogenetic testing plays a critical role in the diagnosis and risk stratification of hematologic malignancies. However, conventional techniques are inherently constrained by technical limitations, including low resolution, labor-intensive workflows, and inter-observer variability. Recent advances in artificial intelligence, particularly deep learning-based approaches, have shown promise in addressing these limitations by enhancing image analysis, automating interpretation, and standardizing complex workflows. Many studies have demonstrated that AI-integrated platforms significantly reduce diagnostic turnaround time, detect cryptic or subclonal chromosomal aberrations, and improve interpretive concordance across laboratories. Despite these advantages, barriers such as limited model interpretability, data heterogeneity, and regulatory challenges remain. Rather than replacing human expertise, AI is emerging as a powerful adjunct that strengthens the accuracy and reproducibility of genomic assessments and promotes timely, individualized therapeutic decision-making. As the technology matures, AI is expected to become an integral component of cytogenetic diagnostics, driving a shift toward more efficient, scalable, and precision-guided clinical workflows in hematologic oncology.

This paper investigates the integration of active reconfigurable intelligent surfaces (ARISs) with unmanned aerial vehicles (UAVs) in a mixed free-space optics radio frequency (FSO-RF) downlink communication system, enabling simultaneous lightwave information and power transfer (SLIPT). The proposed architecture addresses key challenges in UAV-based networks, including limited endurance and backhaul constraints, by allowing the UAV to harvest energy from the optical backhaul while transmitting RF signals enhanced via ARIS to ground users. The system design aims to maximize the minimum achievable rate among users by jointly optimizing the UAV’s beamforming strategy, 3D placement, ARIS reflection coefficients, optical ground station (OGS) transmit power and the power splitting (PS) ratio at the UAV. An alternating optimization framework is developed to decompose the resulting non-convex problem into efficiently solvable subproblems using inner approximation techniques. Simulation results confirm that the proposed approach significantly outperforms baseline schemes, such as passive RIS, fixed UAV deployment, and static PS configurations, delivering improved rate fairness and energy efficiency. These results demonstrate the potential of ARIS-assisted SLIPT-enabled UAVs to support robust and sustainable downlink communications in next-generation wireless networks.

Voice biomarkers hold potential for early cognitive disorder detection, but variations in recording conditions across different environments present challenges for accurate diagnosis using artificial intelligence (AI) models. This study aims to develop a robust, generalizable model for reliably diagnosing cognitive impairments across varied datasets. We implemented a domain generalization approach using an adapted Deep Domain-Adversarial Image Generation (DDAIG) framework. This method transforms input data to reduce center-specific characteristics and emphasizes domain-invariant features, allowing the model to focus on cognitive impairment indicators. Before applying domain generalization, both cognitive impairment (CI) and center classification models achieved accuracies of 0.96. After implementing domain generalization, the CI classification accuracy decreased to 0.90, while the center classification model's accuracy dropped to 0.64. This reduction in the center classification metrics reflects the model's reduced dependence on center-specific features, indicating effective domain generalization. The adapted DDAIG framework effectively reduced center-specific learning, enhancing the model's ability to generalize cognitive impairment classifications across different centers. These findings suggest the role of domain generalization in developing reliable AI diagnostic tools for cognitive disorder detection.

Doxorubicin (DOX) is an effective chemotherapeutic drug for breast cancer. Pulsed electromagnetic field (PEMF) stimulation improves the efficacy of chemotherapeutic agents. However, whether PEMF stimulation modulates DOX-induced caspase-2-dependent apoptosis remains unclear. Thus, this study investigated the effect of PEMF exposure on DOX-induced apoptosis in MDA-MB-231 breast cancer cells. MDA-MB-231 cells were seeded, and the growth medium was replaced with DOX-containing medium and was either unstimulated or stimulated with 1 h PEMF sessions thrice a day at 4-h intervals for up to 72 h. Cell viability was measured using the trypan blue dye exclusion assay, and cell cycles were analyzed using flow cytometry. The molecular mechanisms underlying apoptosis were confirmed by western blotting. PEMF stimulation further reduced the viability of DOX-treated MDA-MB-231 cells. Moreover, it increased the percentage of cells in the sub-G1 phase and cleavage of PARP in DOX-treated MDA-MB-231 cells. PEMF enhanced the DOX-induced activation of caspase-3/7/9 and truncation of Bid. Additionally, PEMF exposure further enhanced the DOX-induced activation of caspase-2 in MDA-MB-231 cells. Caspase-2 inhibition not only suppressed the PEMF-mediated increase in Bid truncation and cleavage of caspase-3/7/9 and PARP, but also significantly restored the PEMF-enhanced reduction of the viability of DOX-treated MDA-MB-231 cells. PEMF stimulation can further increase DOX-induced caspase-2 activation, which consequently potentiates Bid truncation and the subsequent activation of the intrinsic apoptotic pathway, further reducing the viability of DOX-treated MDA-MB-231 breast cancer cells.

This paper propose UAV-RIS communication that takes Energy Efficiency (EE) into account in non-terrestrial networks through trajectory optimization of UAV-RIS that jointly considers communication throughput and energy consumption of unmanned aerial vehicles (UAVs). The proposed algorithm optimizes the UAV-RIS trajectory, base station beamforming vector, and RIS beamforming vector by appropriately using Successive Convex Approximation (SCA), Karush-Kuhn-Tucker (KKT) conditions.

Voice, reflecting cerebral functions, holds potential for analyzing and understanding brain function, especially in the context of cognitive impairment (CI) and Alzheimer's disease (AD). This study used voice data to distinguish between normal cognition and CI or Alzheimer's disease dementia (ADD). This study enrolled 3 groups of subjects: 1) 52 subjects with subjective cognitive decline; 2) 110 subjects with mild CI; and 3) 59 subjects with ADD. Voice features were extracted using Mel-frequency cepstral coefficients and Chroma. A deep neural network (DNN) model showed promising performance, with an accuracy of roughly 81% in 10 trials in predicting ADD, which increased to an average value of about 82.0%±1.6% when evaluated against unseen test dataset. Although results did not demonstrate the level of accuracy necessary for a definitive clinical tool, they provided a compelling proof-of-concept for the potential use of voice data in cognitive status assessment. DNN algorithms using voice offer a promising approach to early detection of AD. They could improve the accuracy and accessibility of diagnosis, ultimately leading to better outcomes for patients.

This study explored the effect of basic psychological needs in secondary physical education (PE) classes in South Korea on physical self-efficacy and attitudes toward PE. Data from 296 middle-school students were collected from May to June 2022 in Seoul or Gyeonggi-do. Participants were surveyed via simple random sampling. Confirmatory factor, correlation, and multiple regression analyses were conducted, and reliability was assessed with Cronbach's α. Basic psychological needs had a significant positive effect on physical self-efficacy. Competence and autonomy positively and significantly affected perceived physical competence (β = 0.535 and 0.320, respectively). Basic psychological needs had a significant positive effect on classroom attitudes toward PE. Autonomy and relatedness positively and significantly affected basic attitudes (β = 0.317 and 0.388, respectively) and social attitudes (β = 0.3498 and 0.213, respectively). Physical self-efficacy had a significant positive effect on PE classroom attitudes. Perceived physical competence and physical self-presentation confidence had a positive and significant effect on basic attitudes (β = 0.258 and 0.166, respectively). The results implied that attitudes toward school life can be improved through physical activity in secondary PE classes.

This paper introduces an algorithm for improving the security performance in a system by combining unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs), which are emerging technologies in the context of 6G networks. Multiple flying RISs are utilized in this approach. Among them, a DNN-based algorithm is presented to deploy flying RISs in appropriate locations to cover the maximum number of users possible. Through simulation experiments, the superiority of the proposed algorithm is confirmed in terms of accuracy and execution time. In the future, research will be conducted on algorithms optimizing beamforming from the perspective of the base station (BS) and achieving active beamforming within the RIS to further enhance the security performance.

Monocytes are peripheral leukocytes that function in innate immunity. Excessive triglyceride (TG) accumulation causes monocyte death and thus can compromise innate immunity. However, the mechanisms by which TG mediates monocyte death remain unclear to date. Thus, this study aimed to elucidate the mechanisms by which TG induces monocyte death. Results showed that TG induced monocyte death by activating caspase-3/7 and promoting poly(ADP-ribose)polymerase (PARP) cleavage. In addition, TG induced DNA damage and activated the ataxia telangiectasia mutated (ATM)/checkpoint kinase 2 and ATM-and Rad3-related (ATR)/checkpoint kinase 1 pathways, leading to the cell death. Furthermore, TG-induced DNA damage and monocyte death were mediated by caspase-2 and -8, and caspase-8 acted as an upstream molecule of caspase-2. Taken together, these results suggest that TG-induced monocyte death is mediated via the caspase-8/caspase-2/DNA damage/executioner caspase/PARP pathways.