Republic of Korea Army

Abstract A coaxial compound rotorcraft may experience insufficient blade tip clearances during high-speed lift-offset level flight or high-g maneuvers such as bank turns. Therefore, the aeromechanical loads acting on the coaxial compound rotorcraft during free flight must be investigated. This paper presents a comprehensive analysis of the aeromechanical loads acting on the rotorcraft with a hingeless coaxial rotorcraft that is designed on the base of X2TD. Efficient modeling approaches were employed, including a beam cross-section analysis using the variational asymptotic beam sectional analysis tool, empirical airframe aerodynamic coefficient estimation using DATCOM, and Euler equation-based airfoil table generation using a multi-element sectional analysis. Subsequently, the aeromechanical features were implemented by modeling the main rotor, pusher propeller, fuselage, and empennage in CAMRAD II. Trim strategies for level flight and 2-g bank turns with different lift-offset levels and flight speeds were devised. The results showed that the coaxial compound rotorcraft could achieve a 2-g turn at cruise speeds of over 200 kt, while ensuring the structural integrity of the blade without any significant increase in the total engine power. A trade-off analysis revealed that reducing the lift-offset levels for a high-speed bank turn maneuver relieves the blade tip clearance but requires a higher engine power and a lower lift-to-drag ratio of the rotor.

This study aimed to investigate posture-related variations in skin temperature and hand heat dissipation under simulated microgravity conditions.Eight young males (24 3 y) participated in three posture conditions: standing, supine, and 10 head-down tilt (HDT), which mimics a microgravity environment.Experiments were conducted in a climate chamber (24 1C, 60 5%RH).A water-perfused glove (7C circulating water) was used for the last 30 min during each 60-min trial to quantify hand heat extraction.Mean skin temperature showed no significant difference among the three posture conditions (32.8-32.9C),but the decrease from resting to posture phases was greater in the supine than standing condition (p < 0.01).Trunk temperatures increased significantly during both supine and HDT conditions, whereas finger temperature dropped sharply after glove cooling, with a larger decline in the supine and HDT posture conditions (p < 0.05).Heart rate and blood pressure were significantly higher in the standing posture than in the two recumbent conditions (p < 0.05).Despite physiological redistribution of skin blood flow, hand heat extraction did not differ significantly among the postures (1.56 1.00, 1.48 0.88, and 1.69 0.92 kcalmin -1 , respectively).Thermal sensation, comfort, and dizziness scores showed no significant variation across the three conditions.These findings suggest that posture-induced changes in blood redistribution alter local skin temperature but have limited impact on overall hand cooling efficiency.The results provide fundamental insights into human thermoregulation under simulated microgravity, supporting the design of liquid-coolded garments for future manned space missions.

We present a simple inverse patterning technique for gallium-based liquid metal on paper substrates, where the liquid metal is selectively removed via a chemical reaction with ethanol. This subtractive process enables the formation of desired patterns by eliminating specific regions of a predeposited liquid metal layer. Gallium-based liquid metal has been applied in various flexible electronic devices, including paper electronics, due to its excellent flexibility and conductivity. However, liquid metal presents a unique challenge for patterning on substrates due to the formation of an oxide layer and its high surface tension. The proposed method demonstrates that the desired pattern can be easily achieved by writing directly onto liquid metal on photo paper with an ethanol-based remover pen, displacing the liquid metal along the pen’s path. The inverse pattern can be restored to its original state by covering the liquid metal, allowing for repeatable rewriting. The effect of ethanol on the surface chemistry of the liquid metal inverse patterning on paper is studied using XPS. We also measure the minimum patternable line width of 150 μm and the line angle of 3°. We present machine-based automatic demonstrations of various inverse patterns, including the Taegeuk emblem, a traditional Korean symbol, and lotus flower shapes featuring complex geometries. Finally, a tunable liquid metal voltage divider on the photo paper with tuning ratios of 1:1, 1:2, and 1:3 is demonstrated.

Orthohantavirus hantanense (HTNV) is a zoonotic pathogen transmitted by rodents and the causative agent of hemorrhagic fever with renal syndrome (HFRS) in East Asia. Long-term reservoir population ecology studies have enhanced our understanding of hantavirus infection patterns and support disease risk assessments critical for military and civilian populations in HTNV high-risk areas. Here, we evaluated fluctuations in the population dynamics of Apodemus agrarius, the primary reservoir of HTNV, assessed hantavirus seroprevalence, and conducted a descriptive analysis of HFRS disease risks in the Republic of Korea (ROK). From 2000 to 2019, a total of 12,476 small mammals representing 15 species were captured across locations spanning northern to southern regions. A. agrarius was the most frequently collected species in the ROK, with juvenile populations peaking during the late fall/winter seasons. A high proportion of A. agrarius captured at military installations/training sites near the Demilitarized Zone tested seropositive for immunoglobulin G antibodies against HTNV, whereas none of those trapped in Jeollanam Province were seropositive. Risk assessment identified high-risk HFRS zones in northern Gyeonggi and Gangwon Provinces. This study provides important insights for forecasting heightened viral transmission risks to humans and implementing targeted mitigation strategies in HFRS high-risk regions of the ROK.

This study aimed to validate the Korean version of the Sleep Health Index (K-SHI) using both self-report measures and objective sleep indices from wearable devices. A total of 204 adults (mean age 30.35 ± 9.76 years, 64.71% female) were recruited and categorized into insomnia and good sleeper groups. Participants completed the K-SHI, the Insomnia Severity Index (ISI) and the Pittsburgh Sleep Quality Index (PSQI). Objective sleep indices were collected using a wearable device (Fitbit Inspire 3) for 4 weeks. Confirmatory factor analysis (CFA), and analyses for internal consistency, test-retest reliability, and convergent validity, and group comparisons were conducted. CFA supported a three-factor model (x2 = 78.51(df = 52, p < .01), CFI = 0.963, TLI = 0.952, RMSEA = 0.050, SRMR = 0.049). The K-SHI showed adequate internal consistency (Cronbach's α = 0.766) and test-retest reliability (r = .871, p < .001). The K-SHI total score was significantly associated with ISI (r = - .545, p < .001) and PSQI (r = - .564, p < .001). When correlated with the wearable device, the K-SHI sleep duration subindex was significantly associated with mean total sleep time (r = .170, p < .05), mean sleep onset (r = - .147, p < .05), and total sleep time standard deviation (r = - .168, p < .05). Additionally, the K-SHI total score was associated with mean wake time (r = .160, p < .05) and total sleep time standard deviation (r = - .171, p < .05). The K-SHI total score significantly differentiated the insomnia and good sleeper groups, with notable differences in K-SHI sleep duration and sleep quality subindices, but no significant difference in the K-SHI disordered sleep subindex. These results demonstrate the validity and reliability of the K-SHI, incorporating both subjective self-report measures and objective sleep data from wearable devices.

ABSTRACT Chemotherapy-induced peripheral neuropathy (CIPN) is a common adverse effect of chemotherapy. The objective of this prospective observational study was to examine the association between circadian misalignment (CM), as measured by phase angle difference (PAD) of biological and behavioral rhythms and CIPN. The PAD of cortisol acrophase and actigraphy-based sleep end time in breast cancer patients was measured and categorized into low PAD (n = 11) and high PAD (n = 12) groups based on median value. CIPN was assessed using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-CIPN20 (CIPN20). The assessment of CM revealed that the sleep end time of the low PAD group was more delayed in relation to cortisol acrophase compared to the high PAD group. The low PAD group demonstrated significantly higher CIPN20 global and sensory scale scores compared to the high-PAD group at one month post-chemotherapy, with an estimated group difference of 17.63 ± 4.75 and 27.07 ± 6.70 (p = 0.001 and p < 0.001, respectively). The present findings indicate that the low PAD group, which exhibited a relatively delayed behavioral rhythm with respect to its biological rhythm, displayed an increased susceptibility to CIPN. Further large-sample research is necessary to attain a comprehensive understanding of the mechanisms through which CM affects CIPN.

Deep learning (DL) technology has shown outstanding performance in various fields such as object recognition and classification, speech recognition, and natural language processing. However, it is well known that DL models are vulnerable to data poisoning attacks, where adversaries modify or inject data samples maliciously during the training phase, leading to degraded classification accuracy or misclassification. Since data poisoning attacks keep evolving to avoid existing defense methods, security researchers thoroughly examine data poisoning attack models and devise more reliable and effective detection methods accordingly. In particular, data poisoning attacks can be realistic in an adversarial situation where we retrain a DL model with a new dataset obtained from an external source during transfer learning. By this motivation, we propose a novel defense method that partitions and inspects the new dataset and then removes malicious sub-datasets. Specifically, our proposed method first divides a new dataset into n sub-datasets either evenly or randomly, inspects them by using the clean DL model as a poisoned dataset detector, and finally removes malicious sub-datasets classified by the detector. For partition and inspection, we design two dynamic defensive algorithms: the Sequential Partitioning and Inspection Algorithm (SPIA) and the Randomized Partitioning and Inspection Algorithm (RPIA). With this approach, a resulting cleaned dataset can be used reliably for retraining a DL model. In addition, we conducted two experiments in the Python and DL environment to show that our proposed methods effectively defend against two data poisoning attack models (concentrated poisoning attacks and random poisoning attacks) in terms of various evaluation metrics such as removed poison rate (RPR), attack success rate (ASR), and classification accuracy (ACC). Specifically, the SPIA completely removed all poisoned data under concentrated poisoning attacks in both Python and DL environments. In addition, the RPIA removed up to 91.1% and 99.1% of poisoned data under random poisoning attacks in Python and DL environments, respectively.