This study evaluated the fit of a one-piece dress using a 3D-printed dress form designed to reflect the body shape of middle-aged women and examined its potential for practical application by comparing the results with those from a 3D virtual fitting program. Therefore, a dress form was created based on the body measurements of middle-aged women using 3D body scanning and 3D printing, and an actual one-piece dress was fitted onto it. The same pattern was then simulated in a 3D virtual fitting program. A subsequent visual assessment was performed to compare and analyze the similarity between the 3D virtual fitting and actual fitting results. The analysis revealed that the 3D-printed dress form more accurately replicated the body characteristics of middle-aged women, making it advantageous for evaluating actual wearing comfort and garment fit. In contrast, the virtual fitting program demonstrated limitations in detailed expressions of elements such as wrinkles in specific body areas and fabric properties, resulting in lower consistency with real-world fitting outcomes compared to the dress form. This study confirmed that the 3D-printed dress form for middle-aged women can enhance accuracy in both fit evaluation and garment production processes. Future studies should focus on developing dress forms that accommodate diverse body types and refining virtual fitting technologies to enable more precise garment simulation and evaluation.

The development of high-performance metal filters is essential for maintaining ultra-clean environments in semiconductor manufacturing. In this study, cross-sealed honeycomb filters were fabricated using STS316L powder via material extrusion additive manufacturing (MEAM) for semiconductor gas filtration. The effects of filter geometry (4 or 9 channels) and sintering temperature (850°C, 950°C, or 1,050°C) on performance were examined. First, 4-channel and 9-channel filters sintered at the same temperature (950°C) exhibited similar porosities of 50.08% and 50.57%, but the 9-channel filter showed a higher pressure-drop (0.26 bar) and better filtration-efficiency (3.55 LRV) than the 4-channel filter (0.19 bar and 3.25 LRV, respectively). Second, for filters with the same geometry (4-channel) increasing the sintering temperature reduced porosity from 64.52% to 40.33%, while the pressure-drop increased from 0.13 bar to 0.22 bar and filtration-efficiency improved from 2.53 LRV to 3.51 LRV. These findings demonstrate that filter geometry and sintering temperature are key factors governing the trade-off between air permeability, pressure-drop, and filtration efficiency. This work provides insights and data for optimizing MEAM-based high-performance metal powder filter design.

Rapidly changing environmental factors due to climate change are increasing the uncertainty of crop growth, and the importance of crop yield prediction for food security is becoming increasingly evident in Republic of Korea. Traditionally, crop yield prediction models have been developed by using statistical techniques such as regression models and correlation analysis. However, as machine learning technique develops, it is able to predict the crop yield more accurate than the statistical techniques. This study aims at proposing the onion yield prediction framework to accurately predict the onion yield by using various environmental factor data. Temperature, humidity, precipitation, solar radiation, and wind speed are considered as climate factors and irrigation water and nitrogen application rate are considered as soil factors. To improve the performance of the prediction model, ensemble learning technique is applied to the proposed framework. The coefficient of determination of the proposed stacked ensemble framework is 0.96, which is a 24.68% improvement over the coefficient of determination of 0.77 of the existing single machine learning model. This framework can be applied to the particular farmland so that each farm can get their customized prediction model, which is visualized by the web system.

Background: The forward head posture acts as a factor that can cause various neurovascular and musculoskeletal dysfunctions. But searching for a study on quality of life for patient with forward head posture was challenging. Therefore, this study aims to find the factors that most affect the quality of life in patients with forward head posture. Objectives: The purpose of this study was to investigate the correlations between the cranio-vertebral angle (CVA), neck disability index (NDI), pain, and sternocleidomastoid (SCM) thickness of patients with forward head posture and the quality of life of the patients and to figure out important factors that affect the quality of life of the patients with forward head posture. Design: Cress-sectional study. Methods: To measure the CVA, the angle at which the visible protrusion of C7 and the ear bead were connected was measured, and the neck disorder index was evaluated using the Korean version of NDI. The degree of pain of the subject was measured using a visual-analog scale (VAS). The SCM thickness was measured using an ultrasound imaging device, and the quality of life was evaluated using the Korean version of the World Health Organization quality of life questionnaire (WHOQL-BREF). Results: A significant predictive model showing 88% explanatory power for the dependent variable was confirmed, with an appropriate regression equation being found. The factor that most affected patients' quality of life in the forward head posture was confirmed by the SCM thickness. Conclusion: When applying an intervention to improve a patient's quality of life for patient with forward head posture, an intervention method that improves the SCM thickness should be recommended.

The fall armyworm (FAW), Spodoptera frugiperda J.E. Smith is a noctuid moth endemic throughout the Western Hemisphere that has recently become widespread in sub-Saharan Africa. In Asia, FAW was firstly reported at corn fields in India, SriLanka, Bangladeshi, Miyanmar and Thailand in 2018. In January 2019, FAW was also found in Yunnan province of China. In March 2019, the larvae which could be tentatively identified as FAW were caught at a corn field of Plant Protection Center of Lao PDR, which is located in Vientiane, Laos. Species identification was confirmed by DNA barcoding using the COⅠ segment of the four larvae, which were found to be the haplotype of rice strain (COⅠ-RS). The host strain identity was additionally analysed as a Tpi-C (C-strain allele) by Triosephosphate isomerase gene (Tpi) segment located on the Z sex chromosome. The result shows that the FAW specimens in Lao is the subpopulation of COⅠ-RS/Tpi-C (COⅠ and Tpi haplotype combination). It was reported that COⅠ-CS/Tpi-C were more frequently observed than COⅠ-RS/Tpi-C in Western Hemisphere and Western Africa, but COⅠ-RS/Tpi-C were more frequently observed in Eastern Africa. It can be supposed that the subpopulation of COⅠ-RS/Tpi-C in Lao is one of the subpopulations which have migrated into the Indochinese peninsula from Eastern Africa, with more detailed analysis for more diverse nationwide specimens left.