This study investigates a vision-based autonomous landing algorithm using a VTOL-type UAV. VTOL (Vertical Take-Off and Landing) UAVs are hybrid systems that combine the forward flight capability of fixed-wing aircraft with the vertical take-off and landing functionality of multirotors, making them increasingly popular in drone-based industrial applications. Due to the complexity of control during the transition from multirotor mode to fixed-wing mode, many companies rely on commercial software such as ArduPilot. However, when using ArduPilot as-is, the software does not support the velocity-based GUIDED commands commonly used in multirotor systems for vision-based landing. Additionally, the GUIDED mode in VTOL software is designed primarily for fixed-wing operations, meaning its control logic must be modified to enable position-based control in multirotor mode. In this study, we modified the control software to support vision-based landing using a VTOL UAV and validated the proposed algorithm in simulation using GAZEBO. The approach was further verified through real-world experiments using actual hardware.

Fault detection in electromechanical systems plays a significant role in product quality and manufacturing efficiency during the transition to smart manufacturing. Because collecting a sufficient number of datasets under faulty conditions of the system is challenging in practical industrial sites, unsupervised fault detection methods are mainly used. Although fault datasets accumulate during machine operation, it is not straightforward to utilize the information it contains for fault detection after the deep learning model has been trained in an unsupervised manner. However, the information in fault datasets is expected to significantly contribute to fault detection. In this regard, this study aims to validate the effectiveness of the transition from unsupervised to supervised learning as fault datasets gradually accumulate through continuous machine operation. We also focus on experimentally analyzing how changes in the learning paradigm of the deep learning model and the output representation affect fault detection performance. The results demonstrate that, with a small number of fault datasets, a supervised model with continuous outputs as a regression problem showed better fault detection performance than the original model with one-hot encoded outputs (as a classification problem).

Gaming disorder, also referred to as game addiction, has garnered increasing clinical attention and was officially recognized by the World Health Organization (WHO) in 2018. Previously categorized alongside behavioral addictions such as gambling, gaming disorder shares key characteristics, including compulsive engagement and persistent behavior despite adverse consequences. Psychological risk factors include high impulsivity, emotional dysregulation, and stress, often exacerbating mental health issues like depression and anxiety. Despite its global recognition, research on gaming disorder in Korean adults remains limited, leaving a gap in understanding how individuals exhibit traits associated with the disorder. This study aims to characterize the psychological traits of high-risk individuals for game disorder in Korea and compare them with low-risk individuals. Findings revealed that high-risk individuals are more prone to addictive behaviors such as internet addiction, binge eating, pathological gambling, and nicotine dependence, though not alcohol addiction. They were also characterized by higher impulsivity, lower self-control, and poorer emotion regulation, particularly a reduced use of cognitive reappraisal strategy. Furthermore, high-risk individuals reported elevated levels of stress, depression, and anxiety. These findings highlight potential risk factors for gaming disorder in adults and provide a foundation for developing targeted screening tools and early intervention strategies for at-risk individuals.

Iron oxide (ε-Fe2O3) is emerging as a promising electromagnetic material due to its unique magnetic and electronic properties. This review focuses on the intrinsic properties of ε-Fe2O3, particularly its high coercivity, comparable to that of rare-earth magnets, which is attributed to its significant magnetic anisotropy. These properties render it highly suitable for applications in millimeter wave absorption and high-density magnetic storage media. Furthermore, its semiconducting behavior offers potential applications in photocatalytic hydrogen production. The review also explores various synthesis methods for fabricating ε-Fe2O3 as nanoparticles or thin films, emphasizing the optimization of purity and stability. By exploring and harnessing the properties of ε-Fe2O3, this study aims to contribute to the advancement of next-generation electromagnetic materials with potential applications in 6G wireless telecommunications, spintronics, high-density data storage, and energy technologies.

Fetal Bovine Serum (FBS) plays a crucial role in animal cell culture; however, the increasing number of bovine fetuses used and sacrificed solely for FBS collection has raised ethical concerns globally. The welfare of fetuses during FBS blood collection has become a key focus of debate among animal welfare and ethics organizations worldwide. Previous studies indicate that heat-inactivated coelomic fluid (HI-CF) from the earthworm Perionyx excavatus may serve as a viable FBS alternative in adherent cell cultures. This study evaluates the potential of HI-CF as an FBS substitute during the in vitro maturation (IVM) stage of bovine embryo culture, with a focus on improving developmental rate through antioxidation effects. In this study, 2% HI-CF was incorporated into IVM media, assessing its impact on cell growth, differentiation, and the expression of genes related to antioxidation. The group of 2% of HI-CF exhibited a trend toward increased cleavage and blastocyst development rates compared to the control group. Although antioxidant genes such as NRF2 and GSR showed no statistically significant differences between the control and treatment groups, a trend toward increased expression was observed. Conversely, GPX1 displayed a trend of decreased expression. Notably, IGF1 and NQO1 were significant upregulated (p < 0.05) in the 2% HI-CF group. Additionally, oocytes stained with H2DCFDA showed a significantly reduced ROS levels (p < 0.05) in the 2% HI-CF group compared with controls. These findings suggest that HI-CF's antioxidative effects support enhanced cell growth and blastocyst development rate, surpassing those observed with FBS. Consequently, HI-CF shows promise as an effective alternative to FBS in vitro maturation of bovine oocytes.

Bearing-shaft systems are essential components in various automated manufacturing processes, primarily designed for the efficient rotation of a main shaft by a motor. Accurate fault detection is critical for operating manufacturing processes, yet challenges remain in sensor selection and optimization regarding types, locations, and positioning. Sound signals present a viable solution for fault detection, as microphones can capture mechanical sounds from remote locations and have been traditionally employed for monitoring machine health. However, recordings in real industrial environments always contain non-negligible ambient noise, which hampers effective fault detection. Utilizing a high-performance microphone for noise cancellation can be cost-prohibitive and impractical in actual manufacturing sites, therefore to address these challenges, we proposed a convolution neural network-based methodology for fault detection that analyzes the mechanical sounds generated from the bearing-shaft system in the form of Log-mel spectrograms. To mitigate the impact of environmental noise in recordings made with commercial microphones, we also developed a denoising autoencoder that operates without requiring any expert knowledge of the system. The proposed DAE-CNN model demonstrates high performance in fault detection regardless of whether environmental noise is included(98.1%) or not(100%). It indicates that the proposed methodology effectively preserves significant signal features while overcoming the negative influence of ambient noise present in the collected datasets in both fault detection and fault type classification.

Pavements have historically been used for mobility, but their usage in cities is steadily increasing owing to social and cultural development. Urban development is rapidly accelerating, primarily because of the concentration of the urban population. Additionally, the effects of the urban heat island are intensifying owing to global warming. One of the main factors contributing to this phenomenon is the increase in impermeable layers, such as asphalt and concrete pavements, in city centers. Various technological developments have been conducted to reduce the effects of urban heat islands. This study developed a moisture-retaining asphalt that absorbs moisture by incorporating a highly super-absorbent polymer (SAP) into a porous asphalt mixture, with the aim of alleviating the urban-heat-island effect. The porous asphalt mixture was designed accordingly. When the mixing design was completed, tests for the tensile strength ratio (TSR), asphalt wheel tracking, and indoor water permeability were conducted on the porous asphalt. Moreover, Hamburg wheel tracking and dynamic water acupuncture tests were performed to evaluate the compatibility of SAP moisture-retaining asphalt, and the results were as follows: Depending on the type and content of SAP, we confirmed that the TSR and permeability coefficient decreased as the amount of SAP increased, resulting in a decrease in durability. In addition, thermal characteristics and simulations showed that the SAP asphalt mixture would have a heat island reduction effect. In this paper, guidelines for the blending design of SAP moisture-retaining asphalt are presented with the aim of alleviating the urban heat island phenomenon by ensuring durability while simultaneously reducing surface temperatures.

복숭아유리나방(Synanthedon bicingulata)은 유충이 나무 줄기 속으로 파고 들어가 형성층을 섭식하는 생태적 특징으로 인해 방제가 어려운 해충이다. 현재에는 성충의 발생시기를 지속적인 모니터링을 통해 화학 방제에 의존하고 있다. 따라서, 성충 발생 시기를 예측할 수 있다면 표본조사와 방제 효율을 극대화할 것으로 기대된다. 본 연구에서는 국내에서 발표된 발생 소장 연구의 발생 데이터와 해당 지역의 기온 데이터를 활용, Weibull function 을 이용하여 복숭아유리나방의 성충 발생 최성기를 예측하는 모델을 개발하였다. 또한 개발된 모델과 SSP 미래 기후변화 시나리오를 이용해 미래 기후변화 상황에서 복숭아유리나방의 전국적 발생 양상이 어떻게 변화할지 예측해보았다. 복숭아유리나방의 성충 발생은 온일도일에 따라 예측이 가능하였고 연 중 2회의 성충 최성기가 발생하는 것으로 예측되었다. 이번 연구에서 개발된 모델은 첫 번째와 두 번째 성충 최성기(50% 발생시기)를 국내 전역에서 평균 6.3일, 4.0일 이내로 예측해 예측 정확도가 매우 높았다. 이번 연구 결과는 난방제 해충인 복숭 아유리나방의 방제 효율을 급격히 높혀줄 뿐만아니라, 기후변화에 따른 복숭아유리나방의 발생 변화 예측에도 기여할 수 있을 것으로 기대된다.

Sestrin 2 (SESN2) is a member of the sestrin family of stress-induced proteins that negatively regulate agingassociated biological processes. This study aims to investigate the role of SESN2 in regulating the differentiation potential and senescence of mesenchymal stem cells (MSCs) derived from young and elderly donors. Bulk RNA sequencing revealed a common decline in the SESN2 mRNA levels in MSCs from elderly individuals, which was confirmed via reverse transcription-polymerase chain reaction and western blot analyses. SESN2 knockdown in MSCs from young donors resulted in phenotypic changes similar to those in MSCs from elderly donors, including an enhanced expression of senescence and adipogenic markers and diminished expression of osteogenic markers. To confirm the effect of decreased SESN2 expression on osteogenic and adipogenic differentiation, we induced Sesn2 knockdown in mouse bone marrow-derived MSCs. Sesn2 knockdown suppressed the mRNA expression of osteogenic marker genes, alkaline phosphatase activity, and matrix mineralization. Furthermore, Sesn2 knockdown enhanced mRNA expression of the adipogenic marker genes and intracellular lipid accumulation. These results suggest that a decline in SESN2 expression during aging contributes to the shift of MSC differentiation from osteogenic to adipogenic lineage.

In recent automated manufacturing systems, compressed air-based pneumatic cylinders have been widely used for basic perpetration including picking up and moving a target object. They are relatively categorized as small machines, but many linear or rotary cylinders play an important role in discrete manufacturing systems. Therefore, sudden operation stop or interruption due to a fault occurrence in pneumatic cylinders leads to a decrease in repair costs and production and even threatens the safety of workers. In this regard, this study proposed a fault detection technique by developing a time-variant deep learning model from multivariate sensor data analysis for estimating a current health state as four levels. In addition, it aims to establish a real-time fault detection system that allows workers to immediately identify and manage the cylinder’s status in either an actual shop floor or a remote management situation. To validate and verify the performance of the proposed system, we collected multivariate sensor signals from a rotary cylinder and it was successful in detecting the health state of the pneumatic cylinder with four severity levels. Furthermore, the optimal sensor location and signal type were analyzed through statistical inferences.

Nasopharyngeal stenosis is defined as a morphological transition of narrowing at the nasopharyngeal region. A 2-yearold, castrated male, Korean short hair cat was referred to the animal medical center, Gyeongsang National University. According to clinical signs, diagnostic imaging, and physical examination, nasopharyngeal stenosis was diagnosed. The staphylectomy was performed using a CO2 laser, and there were not any post-operative complications. The patient was discharged in two days. This report describes the case of nasopharyngeal stenosis in cat and represents that laser ablation could be a good option for surgical management of the nasopharyngeal region with a low complication rate.

PURPOSES : There has been increasing interest in South Korea on warm-mix asphalt (WMA) and cold-mix asphalt (CMA) technologies that allow production of asphalt pavement mixtures at comparatively lower temperatures than those of hot-mix asphalt (HMA) for use in pavement engineering. This study aims to evaluate the feasibility of replacing HMA pavement with WMA pavement with the goal of reducing CO2 emissions associated with asphalt production for road construction. METHODS : Changes in the dynamic modulus characteristics of WMA and HMA according to short-term and long-term aging were evaluated. In addition, the effects of water damage were evaluated for short- and long-term aging stages. RESULTS : For WMA, in the process of mixing and short-term aging, early-age dynamic modulus decreased owing to low temperature and reduced short-term aging (STA) time. This could result in early damage to the asphalt pavement depending on the applied traffic load and environmental load. CONCLUSIONS : Mastercurves of the dynamic modulus were used for comparative analysis of WMA and HMA. Compared to the dynamic modulus after STA of HMA, the estimated aging time determined by experiments for WMA to achieve the required stiffness was more than 48 hours, which is equiva-lent to approximately 4 to 5 years real service life when converted. It is considered that further studies are needed for performance optimization to achieve early-age performance of the asphalt mixes.

One of cosmopolitan pest, Agrotis ipsilon, causes serious economic damages in horticultural crops. This study compared the host fitness of A. ipsilon among nine major horticultural crops in Korea. Among the nine crops, the population of A. ipsilon failed to complete its development in spinach, cucumber, melon, and kidney bean. The host effects on development and reproduction of A. ipsilon were further investigated in the remained five crops. Host plants significantly (P < 0.05) affected the development-related factors of A. ipsilon eggs, larvae, and pupae. They also affected the adult reproduction-related factors including preoviposition period, oviposition period and number, and longevity except for the prepupa stage. A positive relationship was found be tween biological factors. Among the nine crops in this study, napa cabbage showed the highest suitability for the A. ipsilon populations. These findings in this study would be helpful to understand the ecology and develop the man agement tactics of A. ipsilon in horticultural crops.

신종 갈색날개매미충은 과실류에 대하여 심각한 경제적 피해를 야기한다. 국내에서는 이종의 관리를 위해 화학약제를 사용하고 있다. 그러나, 이 해충에 대한 살충제의 사용시기과 종류는 꿀벌 개체군에 악영향을 일으킨 다. 그러므로, 본 연구는 감에 있어 살충제의 사용을 감소시키고 효과를 증대시킬 수 있는 방제의사결정 수준을 평가하였다. 공간분포 분석을 통하여 감귤의 피해관련 요소인 신초, 과실형성수, 수확량에 어떤 발육단계가 영향 을 주었는지를 확인하였다. 갈색날개매미충 알의 분포는 감 열매 수와 공간적으로 관련되어 있었다. 그러나 갈색 날개매미충 알의 밀도와 감의 피해와 관련된 요인들 간에 어떠한 선형 관계를 발견하지는 못했다. 그러나 갈색날 개매미충의 밀도와 산란된 죽은 가지와의 상관성은 확인되었다. 난괴 밀도에 근거한 죽는 가지 추정 발육 모델 (Developed model of branch death possibility)로부터 새롭게 발달된 가지당 5.75개의 난괴가 방제의사결정 수준으 로 제안되었다. 위의 결과들은 과원내 갈색날개매미충 관리의 효과를 높임과 동시에 다른 곤충에 대한 방제의사 결정수준을 개발하는데 도움을 줄 수 있을 것이다.