Background: The progressive digitalization of contemporary life, coupled with prolonged sedentary behavior, has contributed to the widespread occurrence of forward head posture (FHP) in the general population. Among the various exercise interventions aimed at correcting FHP, stretching exercises and proprioceptive training have garnered significant attention. Objectives: To investigate the effects of stretching exercises and proprioceptive training on craniovertebral angle (CVA), an indicator of forward head posture (FHP), and on the muscle tone of the suboccipital muscles. Design: Randomized controlled trial. Methods: Thirty participants were randomly assigned to either the stretching exercise group (n=15) or the proprioceptive training group (n=15), and each group underwent intervention three times per week for six weeks. CVA and muscle tone of the suboccipital muscles were measured before and after the six-week intervention, and the results were statistically analyzed for comparison. Results: Both groups showed a significant increase in CVA after the intervention (P<.05), with no significant difference between the groups. The muscle tone of the suboccipital muscles did not show statistically significant changes either within or between the groups. Conclusion: Six weeks of stretching exercise and proprioceptive training both resulted in a significant increase in CVA, demonstrating that both interventions are effective in improving FHP. However, neither intervention produced significant changes in the muscle tone of the suboccipital muscles.

Anomaly detection technique for the Unmanned Aerial Vehicles (UAVs) is one of the important techniques for ensuring airframe stability. There have been many researches on anomaly detection techniques using deep learning. However, most of research on the anomaly detection techniques are not consider the limited computational processing power and available energy of UAVs. Deep learning model convert to the model compression has significant advantages in terms of computational and energy efficiency for machine learning and deep learning. Therefore, this paper suggests a real-time anomaly detection model for the UAVs, achieved through model compression. The suggested anomaly detection model has three main layers which are a convolutional neural network (CNN) layer, a long short-term memory model (LSTM) layer, and an autoencoder (AE) layer. The suggested anomaly detection model undergoes model compression to increase computational efficiency. The model compression has same level of accuracy to that of the original model while reducing computational processing time of the UAVs. The proposed model can increase the stability of UAVs from a software perspective and is expected to contribute to improving UAVs efficiency through increased available computational capacity from a hardware perspective.

목적: 본 연구는 대학생에서 콘택트렌즈 착용 여부에 따라 음주와 전자기기 사용이 안구건조 증상에 미치는 영향을 조사하였다. 방법 : 대학생 32명(64안)을 콘택트렌즈 착용군(16명)과 미착용군(16명)으로 나누었다. 순목 횟수, 비침입성 눈 물막 파괴검사(NIBUT), 오큐튜브 검사를 기본 검사, 스마트폰 20분 시청 후, 목표 혈중 알코올 농도 0.03% 달성 후 측정하였다. 결과: NIBUT는 미착용군에서 착용군보다 일관되게 낮았으며, 기본 검사 및 전자기기 사용 후 유의한 차이를 보였다 (p=0.010, p=0.001). 음주 후에는 경계선상의 유의성을 보였다(p=0.057). 오큐튜브 검사값은 미착용군에서 높았으며, 음주 후 유의한 차이를 나타냈다(p=0.016). 미착용군은 전자기기 사용 후 순목 횟수와 오큐튜브 검사값에서 유의한 변화를 보였고(p=0.001, p=0.000), 음주 후에는 모든 검사값에서 유의한 변화가 나타났다(p=0.039, p=0.000, p=0.000). 착용군은 전자기기 사용 후 순목 횟수(p=.034), 음주 후 오큐튜브 검사(p=0.008)에서 유의한 차이를 보였다. 건성안의 유병률은 미착용군에서 전자기기 사용 후(p=0.028), 착용군에서 음주 후 (p=0.022) 유의하게 증가하였다. 결론 : 음주와 전자기기 사용은 콘택트렌즈 착용 여부와 관계없이 대학생의 안구건조 증상을 악화시킬 수 있다.

본 논문은 라이소자임이 칸디다 알비칸스에 항균효능이 있음을 발견하였고 라이소자임의 분해 절편도 또한 항균효능이 있는지 단백질 분해 효소인 트립신을 처리하여 확인해 보았다. 130개의 아미노산 으로 구성된 14 kDa 단백질인 라이소자임을 약 24 kDa의 분자량을 가지는 단백질 분해 효소인 트립신으 로 분해 하였다. 분해된 생성물은 고성능 액체 크로마토그래피인 분석용 및 제조용 HPLC를 사용하여 분 석 및 분리하였다. 이를 통해 효율적인 라이소자임 소화를 위한 최적 조건을 확인하고, 분해된 펩타이드 절 편들을 분리하였다. 효능평가 결과, 일부 펩타이드 절편들이 칸디다 알비칸스에 강한 항균 활성을 보임을 확인하였으며 몇몇은 활성이 줄어드는 결과를 보였다.

In recent years, high-entropy alloys (HEAs) have attracted considerable attention in materials engineering due to their unique phase stability and mechanical properties compared to conventional alloys. Since the inception of HEAs, CoCrFeMnNi alloys have been widely investigated due to their outstanding strength and fracture toughness at cryogenic temperatures. However, their lower yield strength at room temperature limits their structural applications. The mechanical properties of HEAs are greatly influenced by their processing methods and microstructural features. Unlike traditional melting techniques, powder metallurgy (PM) provides a unique opportunity to produce HEAs with nanocrystalline structures and uniform compositions. The current review explores recent advances in optimizing the microstructural characteristics in CoCrFeMnNi HEAs by using PM techniques to improve mechanical performance. The most promising strategies include grain refinement, dispersion strengthening, and the development of heterogeneous microstructures (e.g., harmonic, bimodal, and multi-metal lamellar structures). Thermomechanical treatments along with additive manufacturing techniques are also summarized. Additionally, the review addresses current challenges and suggests future research directions for designing advanced HEAs through PM techniques.