목적 : 코로나19 팬데믹 기간 동안 젊은 성인을 대상으로 설문조사를 시행하여 스마트폰 사용 시간과 건성안 유병률 사이의 연관성을 평가하고자 하였다. 방법 : 대학생들을 대상으로 설문조사를 실시하였고 안구건조증은 OSDI 설문지를 이용하여 평가하였다. 안구 건조증은 OSDI 점수에 따라 경도(13~22), 중등도(23~32), 중증(33~100) 건성안으로 분류하였다. 결과 : 완성된 설문지는 총 282개를 받았지만, 기준에 맞지 않는 응답자를 제외하고 157개 설문지를 분석했다. 본 연구에서 코로나19 팬데믹 기간 동안 응답자의 77%가 스마트폰 사용 시간이 증가하였고, OSDI 을 통한 안구 건조 진단결과는 98명(62.4%)이 정상, 30명(19.1%)이 경도, 16명(10.2%)이 중등도, 13명(8.3%)이 중증인 건성안으로 밝혀졌다. OSDI는 스마트폰 사용시간 증가와 유의한 상관관계를 보였다(r=0.241, p=0.012). 결론 : 건성안 유병률은 157명 중 37.6%로 나타났고 안구건조증은 스마트폰 사용 시간의 증가와 관련이 있는 것으로 나타났다.

Pacific herring, Clupea pallasii, a keystone species with significant ecological and commercial importance, is declining globally throughout much of its range. While traditional fishing equipment methods remain limited, new sensitive and rapid detection methods should be developed to monitor fisheries resources. To monitor the presence and quantity of C. pallasii from environmental DNA (eDNA) extracted from seawater samples, a pair of primers and a TaqMan® probe specific to this fish based on mitochondrial cytochrome b (COB) sequences were designed for the real-time PCR (qPCR) assay. The combination of our molecular markers showed high specificity in the qPCR assay, which affirmed the success of presenting a positive signal only in the C. pallasii specimens. The markers also showed a high sensitivity for detecting C. pallasii genomic DNA in the range of 1 pg~100 ng rxn-1 and its DNA plasmid containing COB amplicon in the range of 1~100,000 copies rxn-1, which produced linear standard calibration curves (r2=0.99). We performed a qPCR assay for environmental water samples obtained from 29 sampling stations in the southeastern coastal regions of South Korea using molecular markers. The assay successfully detected the C. pallasii eDNA from 14 stations (48.2%), with the highest mean concentration in Jinhae Bay with a value of 76.09±18.39 pg L-1 (246.20±58.58 copies L-1). Our preliminary application of molecular monitoring of C. pallasii will provide essential information for efficient ecological control and management of this valuable fisheries resource.

This study aimed to develop Lautropia mirabilis -specific quantitative real-time polymerase chain reaction (qPCR) primers based on the sequence of DNA-directed RNA polymerase subunit beta gene. The PrimerSelect program was used in designing of the qPCR primers, RTLam-F4 and RTLam-R3. The specificity of the qPCR primers were performed by conventional PCR with 37 strains of 37 oral bacterial species, including L. mirabilis . The sensitivity of the primers was determined by qPCR with the serial dilution of purified genomic DNA of L. mirabilis KCOM 3484, ranged from 4 ng to 4 fg. The data showed that the qPCR primers could detect only L. mirabilis strains and as little as 40 fg of genome DNA of L. mirabilis KCOM 3484. These results indicate that this qPCR primer pair (RTLam-F4/ RTLam-R3) may be useful for species-specific detection of L. mirabilis in epidemiological studies of oral bacterial infectious diseases such as periodontal disease.

The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.

Recently, there has been growing interest in harmful substances released from household items such as volatile organic compounds (VOCs) and this has increased people’s environmental awareness. In this study, adhesives and manicures were used as samples of indoor household goods and formaldehyde emission and tested over time under temperature conditions of 15oC, 25oC, 35oC, and 45oC. The small chamber method as the indoor air quality process test method was employed and used to evaluate the concentration of formaldehyde emissions. As a result, formaldehyde emissions gradually decreased over time in both tests using adhesives and manicures. The cumulative emission showed a logarithmic function over time, and the formaldehyde can be released for longer periods of time at lower temperature conditions. The logarithmic value and response time showed linear relationships, and it can be inferred that the formaldehyde was released from the sample through the first order reaction. Furthermore, the relationship between temperature and velocity constants which was determined using the Arenius linear equation showed that the reaction rate of formaldehyde can be estimated by a temperature change.

In this study, the purpose of this study was to examine the effect of twisting in the preparation of waterproofing in the process of unfolded donut-type staking method fire hoses in indoor hydrant system. The central pull-out method caused more twisting than the rolling method, and there was no significant difference in the number of twists according to the pull-out method in the case of male and female students. It was found that the time it took to untwist and prepare waterproofing was much shorter for male students. The angle valve and hose are connected, and the time to untwist and prepare for waterproofing after withdrawing the fire hose with the hose and nozzle connected was shorter than the unconnected state. In the rolling method, when a hose connected with two 15 m fire hoses was used and the angle valve-hose was connected, but the hose-nozzle was not connected, the least kinking occurred. The time to untwist and prepare for waterproofing was also the shortest. As a result, in the withdrawal method of the donut-type loaded fire hose in the indoor hydrant system, it is a rolling method rather than a central withdrawal method. With the angle valve and hose connected, unfold the fire hose with the hose and nozzle connected, if a large number of people unwind the twisted hose, the time to prepare for waterproofing can be shortened.

This study developed a scenario to understand the reaction rate and operational time according to RTI value of rate of rise detector in each type in case of fire mattress. In the results of analyzing the reaction rate and operational time of detector in each scenario, in case when installing a single detector, the elevated temperature per minute was raised to 8℃/min ~ 9℃/min. In case when installing two detectors, it was raised to 9℃/min ~ 10℃/min. In case when installing three detectors, it was raised to 10℃/min. The horizontal distance between detector and mattress was 1.8m~2.5m. Whenever the number of detectors was increased, the horizontal distance was decreased. The operational time of detector was within maximum 540 seconds and minimum 420 seconds. As the research tasks in the future, there should be the researches on the effects of reaction rate of detector on the evacuation in case of fire through the result value of RSET by setting up the latency until the detector operates, and the researches on the safety by understanding if the operational time of detector is suitable for the evaluation standard of performancecentered design.

This study analyzed the effect of time of trot on hematology and blood chemistry values of the Jeju Pony crossbreed horses that are commonly used for riding (14.1±1.4 years old, Gelding). A total of 28 parameters including vital signs as well as stress hormones such as cortisol and lactic acid levels were examined as the time of the trot exercise progressed. Vital signs such as heart rate (38.0→81.0 times/min) and respiratory rate (11.7→35.7 times/min) increased significantly within 30 minutes of exercise. However, difference in the body temperature was not observed before and after exercise. The hematology including white blood cell count (8.03→9.52×103 cells/μL), red blood cell count (5.94×103→7.23–7.32×103 cells/μL), hemoglobin levels (11.82→14.65–14.78 g/dL), and hematocrit levels (25.04→30.27%) significantly increased 30 minutes after the start of the exercise (p<0.05). The blood chemistry value of albumin (3.25→3.47 g/dL) (p<0.05) only showed a significant increase after the exercise. However, the other blood chemistry levels such as, Na+, K+, Ca2+, total CO2, creatine kinase, glucose, blood urea nitrogen, creatinine, aspartate transaminase, total bilirubin, gamma–glutamyl transpeptidase, and total plasma protein did not change. Also, cortisol and lactic acid levels did not show significant difference. The middle-aged Jeju pony crossbreed horses were not stressed by the 30-minute exercise; therefore, it can be concluded that there is no problem regarding the safety of both the rider and the animal.

본 연구에서 multiplex PCR과 real-time PCR을 이용하여 창난젓의 원료를 감별할 수 있는 새로운 판별법을 개발하였다. 명태와 가이양의 종 특이 프라이머를 디자인하고, 명태와 가이양의 genomic DNA를 template로 single PCR과 multiplex PCR을 실시하였다. PCR을 실시한 결과, single PCR에서 명태(297 bp)와 가이양(132 bp)에 해당하는 PCR 밴드를 확인하였으며 교차 반응이 일어나지 않는 것을 확인하였다. Multiplex PCR에서 명태와 가이양 사이에 교차 반응없이 증폭이 일어나는 것을 확인하였다. Real-time PCR 결과, 명태 종 판별 프라이머에서 명태의 Ct 평균값은 20.765±0.691, 가이양 시료에서 Ct 평균값은 35.719±1.828이 었으며, 가이양 종 판별 프라이머에서 명태 시료의 Ct 평균 값은 35.996±1.423, 가이양 시료의 Ct 평균값은 20.096±0.793 으로 프라이머의 효율성, 특이성 및 교차 반응성에서 유의한 차이가 나타났다. 이러한 결과를 바탕으로 시중에서 판매되는 7개 제품을 multiplex PCR 및 real-time PCR로 확인 하였으며, 모든 시료에서 유효한 결과를 확인하였다. 본 연구에서 제작된 명태와 가이양에 대한 종 특이적 프라이머는 가공된 젓갈 시료의 원료의 판별 가능하며, 이러한 결과는 식품안전관리에 기여할 수 있을 것으로 기대된다.

본 연구에서는 지방 산화를 억제하고 품질이 우수한 부세 굴비의 가공법을 개발하기 위해 대두와 멸치의 복합 발효소재를 첨가한 염지제 처리 및 건조 시간에 따른 부세 굴비를 제조하여 품질 특성을 비교하였다. 발효소재 첨가에 따른 염지제와 부세 굴비의 DPPH 및 ABTS 라디칼 소거능은 발효소재의 함량에 따라 증가하였으나, 발효소재 1% 이상에서 부세 굴비의 라디칼 소거능은 유의적인 차이를 보이지 않았다. 발효소재의 함량이 증가할수록 부세 굴비의 아미노태질소 함량은 증가하고 VBN, TBARS, 산가는 감소하는 결과를 나타내나, 발효소재 1% 이상에서 품질의 유의적인 차이가 크지 않으므로 염지제의 발효소재 함량은 1%로 선정하였다. 염지제의 염도에 따른 부세 굴비의 품질을 분석한 결과 염도가 증가할수록 아미노태질소 함량은 증가하고 히스타민, VBN, TBARS, 산가는 감소하는 결과는 나타내며, 염지제의 염도는 품질 개선 효과가 가장 우수한 7% 조건을 선정하였다. 염지 처리 후 건조 시간에 따른 부세 굴비의 품질을 분석한 결 과 건조 시간이 증가할수록 수분은 감소하고 염도, pH, 아미노태질소, 히스타민, VBN, TBARS, 산가는 증가하였다. 그러나 건조 48시간 이하는 수분이 많아 저장성이 낮으며, 건조 96시간에서 히스타민과 산패도의 증가율이 높아지므로 부세 굴비의 건조 시간은 72시간이 적합한 것으 로 판단되었다.

본 연구는 베이비부머를 대상으로 미래시간조망과 주관적 안녕감의 관계에서 희망의 조절효과를 검증하는 것이다. 이를 위하여 D시의 베이비 부머(1955~1963년생)에게 면접식 설문조사를 실시하여 총 380부를 분석에 활용하였다. 자료분석은 SPSS 19.0과 PROCESS macro 3.3 통계프로그램을 사용하여 기술통계분석, 각 변인 간 상관관계분석, 희망의 조절효과를 알아보기 위한 회귀분석을 실시하였다. 주요 연구결과는 다음과 같다. 첫째, 미래시간조망은 주관적 안녕감에 유의미한 영향을 미치는 것으로 나타났다. 둘째, 미래시간조망과 주관적 안녕감의 관계에서 희망의 조절효과가 통계적으로 유의하게 나타났다. 이러한 결과는 베이비부머의 미래시간조망을 확장함으로써 주관적 안녕감을 높일 수 있고, 미래시간 조망 확장수준이 낮더라도 희망을 다양한 경로로 활용한다면 주관적 안녕감을 높일 수 있음을 나타내는 것이다. 이러한 통합적 경로확인을 바탕으로 베이비부머의 주관적 안녕감을 높일 수 있는 다양한 방법을 모색하였다.

수역 내 충돌 위험 식별은 항해의 안전을 위해 중요하다. 본 연구에서는 거리 요인을 기반으로 한 군집화 방법인 계층 클러스 터링을 포함하는 새로운 충돌 위험 평가 방법을 도입했으며, 주변의 선박이 많은 경우 실시간 데이터, 그룹 방법론 및 예비 평가를 사용하여 선박을 분류하고 충돌위험평가를 기반으로 평가하였다(HCAAP 처리라 부른다). 조우하는 선박들의 군집은 계층 프로그램에 의해 모아지고, 예비 평가와 결합되어 상대적으로 안전한 선박을 걸러내었다. 그런 다음, 각 군집 내에서 조우하는 선박 사이의 최근접점(DCPA) 및 최근접점까지의 도착시간(TCPA)까지의 시간을 계산하여 충돌위험지수(CRI)와의 관계를 비교하였다. 조우하는 선박들간의 군집에서 CRI와 DCPA 및 TCPA 수학적 관계는 음의 지수 함수로 구성되었다. 이러한 CRI로부터 운영자는 명시된 해역에서 항해하는 모든 선박의 안전성을 보다 쉽게 평가할 수 있으며, 프레임워크는 해상운송의 안전과 보안을 개선하고 인명 및 재산 손실을 줄일 수 있다. 본 연구에 서 제안된 프레임워크의 효과를 설명하기 위해 국내의 목포 연안 해역에서 실험 사례 연구를 수행하였다. 그 결과, 본 연구의 프레임워크가 각 군집 내에서 조우 선박 간의 충돌 위험 지수를 탐지하고 순위를 매기는 데 효과적이고 효율적이라는 것을 보여 주었으며, 추가연구를 위한 자동 위험 우선순위를 지정할 수 있게 해주었다.

During the shift from gasoline vehicles to electric ones, auto parts manufacturing companies have realized the importance of improvement in the manufacturing process that does not require any layout changes nor extra investments, while maintaining their current production rate. Due to these reasons, for the auto part manufacturing company, I-company, this study has developed the simulation model of the PUSH system to conduct a process analysis in terms of production rate, WIP level, and logistics work’s utilization rate. In addition, this study compares the PUSH system with other three manufacturing systems -KANBAN, DBR, and CONWIP- to compare the performance of these production systems, while satisfying the company’s target production rate. With respect to lead-time, the simulation results show that the improvement of 77.90% for the KANBAN system, 40.39% for the CONWIP system, and 69.81% for the DBR system compared to the PUSH system. In addition, with respect to WIP level, the experimental results demonstrate that the improvement of 77.91% for the KANBAN system, 40.41% for the CONWIP system, and 69.82% for the DBR system compared to the PUSH system. Since the KANBAN system has the largest impacts on the reduction of the lead-time and WIP level compared to other production systems, this study recommends the KANBAN system as the proper manufacturing system of the target company. This study also shows that the proper size of moving units is four and the priority allocation of bottleneck process methods improves the target company’s WIP and lead-time. Based on the results of this study, the adoption of the KANBAN system will significantly improve the production process of the target company in terms of lead-time and WIP level.

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

This paper aims to investigate the construction of ideal leadership in newspaper editorials from four American newspapers. In times of global crisis caused by COVID-19, editorials dealing with national leaders' performances show their opinions and attitudes toward President Trump by making evaluative comments. Previous studies regarding evaluative characteristics embedded in newspaper editorials have focused on the frequent use of modality. By analyzing editorials drawn from major American newspapers, the present study shows that not only words of modality but also statements without any modal expressions play a crucial role in representing and evaluating President Trump's leadership. The analysis also demonstrates that the combination of modal and non-modal statements serves to form writers' perspectives and create the ideology of how ideal and competent leaders should act when faced with a crisis.