Forecasting port container throughput is crucial due to its impact on economic development. Socio-economic factors, which introduce uncertainty, are increasingly integrated into throughput forecasting. The complexity of common multivariate forecasting models significantly affects accuracy, yet few studies compare their performance on the same time series for throughput modeling. This study implements, evaluates, and compares the performance of eight multivariate forecasting models for port throughput within a proposed multiple-input single-output (MISO) system, chosen for their frequent use in container throughput research. It investigates two data preprocessing approaches: Random Forest Variable Importance Method (RF-VIM) and a Multi Lagged Value approach. The comparison uses six error metrics: normalized root mean squared error, mean absolute error, mean absolute percentage error, mean error, and root mean percentage error. Performances are discussed, and recommendations for adopting a suitable model are provided.

흉골 자기공명영상 검사 시 호흡 등 환자 움직임에 의한 인공물 발생을 최소화하는 것은 어렵다. 하지만 자기공명영 상 검사는 타 영상 검사와 비교해 흉골 병변을 발견하는 데 있어 진단적 가치가 높은 장점이 있다. 따라서 본 연구에 서는 환자의 검사 자세 및 딥러닝 기법을 통해 최적의 검사 방법을 도출하고자 한다. 자세 별 영상 변화를 확인하기 위해 바로 누운 자세, 엎드린 자세, 유방 코일을 사용한 엎드린 자세로 진행하였으며, 고식적 기법의 영상과 Deep Resolve Boost(DRB) 기법을 적용한 영상을 비교 관찰하였다. 모든 대상에게 같은 조건으로 각 영상을 2회씩 획득 한 후 전반적인 영상 품질을 기준으로 정성적으로 평가하였고, DRB의 적용 여부에 따른 신호 대 잡음비의 변화 정도를 정량적으로 평가하여 개선 정도를 산출하였다. 정성적 평가에서 DRB 적용 여부와 무관하게 엎드린 자세, 유방 코일을 사용한 엎드린 자세, 바로 누운 자세 순으로 높은 점수를 얻었으며, DRB를 적용한 영상이 고식적 기법 의 영상보다 높은 점수를 얻었다. 또한 정량적 평가를 통해 유방 코일을 사용한 엎드린 자세, 엎드린 자세, 바로 누운 자세 순으로 높은 개선 정도를 확인하였다. 본 연구를 통해 흉골 검사 시 DRB 기법을 적용하는 것은 영상의 질을 높이는 방법임을 확인하였다. DRB를 적용하지 못하는 환경에서는 될 수 있으면 엎드린 자세를 적용하는 것을 권고하며, DRB를 적용할 수 있는 환경에서는 환자 측 인자를 고려하여 엎드린 자세와 유방 코일을 사용한 엎드린 자세를 모두 적용할 수 있다.

Purpose: Nursing students' competence in virtual simulation-based learning is a key factor in its success. This study explored the validity and reliability of a virtual-simulation-based learning competency self-evaluation tool for nursing students. Methods: Data were collected from a web-based survey. First, 11 nursing professors participated in a focus group interview, and 7 simulation education experts participated in the preliminary item content validity. The participants in these two aspects were not the same. Then, a preliminary survey was conducted with 15 fourth-year nursing students in I City. Next, based on these three efforts, a final survey comprising 20 evaluation items was developed. This survey was administered to third- and fourth-year nursing students at four nursing colleges in Korean provinces (Seoul, Gyeonggi, Gangwon, and Gyeongsan-do); 222 complete questionnaires were used for the final analysis. Further, Kirkpatrick’s evaluation model was used for four steps each of tool development and verification processes of the associated psychometric aspects, for a total of eight steps. An exploratory factor analysis was performed on the collected survey data, and verify the tool's validity and reliability. Results: Four factors comprising 15 items explained 66.59% of the variance: learning preparation and start-up (4 items), nursing assessment (3 items), data interpretation (3 items), and problem solving (5 items). The Cronbach's α of the tool was 0.74, and that of the factors ranged from 0.72 to 0.80. Conclusions: The tool's validity and reliability were demonstrated using established methodologies. This tool can be useful for evaluating Korean nursing students' virtual simulation learning competence.

Gas identification techniques using pattern recognition methods were developed from four micro-electronic gas sensors for noxious gas mixture analysis. The target gases for the air quality monitoring inside vehicles were two exhaust gases, carbon monoxide (CO) and nitrogen oxides (NOx), and two odor gases, ammonia (NH3) and formaldehyde (HCHO). Four MEMS gas sensors with sensing materials of Pd-SnO2 for CO, In2O3 for NOX, Ru-WO3 for NH3, and hybridized SnO2-ZnO material for HCHO were fabricated. In six binary mixed gas systems with oxidizing and reducing gases, the gas sensing behaviors and the sensor responses of these methods were examined for the discrimination of gas species. The gas sensitivity data was extracted and their patterns were determined using principal component analysis (PCA) techniques. The PCA plot results showed good separation among the mixed gas systems, suggesting that the gas mixture tests for noxious gases and their mixtures could be well classified and discriminated changes.