• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.3
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• pp.237-244
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• 2022

Onions are a significant crop in Korea, and cultivation is increasing every year along with high demand. Onions are planted in the fall and mainly harvested in June, the rainy season, therefore, physiological changes in onion bulbs during long-term storage might have happened. Onions are stored in cold room and at adequate relative humidity to avoid quality loss. In this study, bio-yield stress and weight loss were measured as the quality parameters of net packaged onions during 10 weeks of storage, and the storage environmental conditions are monitored using sensor networks systems. Quality estimation of net packaged onion during storage was performed using the storage environmental condition data through machine learning approaches. Among the suggested estimation models, support vector regression method showed the best accuracy for the quality estimation of net packaged onions.

• Advances in materials Research
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• v.11 no.1
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• pp.59-73
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• 2022

In the construction industry, thin-walled frame elements with very slender open cross-sections and low torsional stiffness are often subjected to a complex loading condition where axial, bending, shear and torsional stresses are present simultaneously. Hence, these often fail in instability even before the yield capacity is reached. One of the most common instability conditions associated with thin-walled structures is Lateral Torsional Buckling (LTB). In this study, a first order Generalized Beam Theory (GBT) formulation and numerical analysis of cold-formed steel lipped channel beams (C80×40×10×1, C90×40×10×1, C100×40×10×1, C80×40×10×1.6, C90×40×10×1.6 and C100×40×10×1.6) subjected to uniform moment is carried out to predict pure Lateral Torsional Buckling (LTB). These results are compared with the Finite Element Analysis of the beams modelled with shell elements using ABAQUS and analytical results based on Euler's buckling formula. The mode wise deformed shape and modal participation factors are obtained for comparison of the responses along with the effect of varying the length of the beam from 2.5 m to 10 m. The deformed shapes of the beam for different modes and GBTUL plots are analyzed for comparative conclusions.

• Natural Product Sciences
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• v.28 no.3
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• pp.130-137
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• 2022

This study was conducted to characterise phytochemicals and to explore the biological activities of Vitex negundo leaves. The washed, course powder of V. negundo leaves were extracted with different solvents of increasing polarity. All the extracts were characterized and biological activities were compared. The results revealed that the ethanolic and cold water extracts showed the presence of all phytochemicals studied except protein compared to other extracts. Further, the quantitative estimation of phytochemicals showed that the ethanolic extract had highest yield and maximum amount of total polyphenols, flavonoids, and alkaloids with the least amount of tannins compared to other extracts studied. Furthermore, the highest total polyphenol content corresponds with the potent biological activities. Indeed, in vitro antioxidant and antisteroidogenic activities were highest in the ethanolic extract than others. To conclude, the present study is the first to report the characterization and antiandrogenic property of V. negundo leaf extracts. The ethanolic extract of V. negundo leaves can be used as an antioxidant and antiandrogenic agent. Hence, it can be considered for the treatment of hyperandrogenic conditions like polycystic ovary syndrome, etc.

• Korean Journal of Pharmacognosy
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• v.53 no.3
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• pp.119-124
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• 2022

Vaginal microbiome deeply influences vaginal health via production of messenger molecules. The healthy vaginal pH ranges between 3.5 and 4.5. However, dysbiosis of vaginal microbiome increases the pH level, leading to the incidence of vaginitis. The commensal vaginal bacterium Dermabacter vaginalis-which was isolated from the vaginal fluid of a Korean female-was incubated in acidic and neutral pH to simulate healthy and vaginitis conditions, respectively. The chemical profiles of the two different cultures were compared using HPLC. The compound showing distinctive difference between the two sets of data was presumed to be a chemical messenger, which was identified as cyclo(L-pro-L-met) by analysis of NMR, MS, and specific rotation data. Synthesis was achieved in three steps (overall yield 15%), enabling structure confirmation and antimicrobial evaluation against vaginal pathogens. Cyclo(L-pro-L-met) showed antifungal activity against Candida albicans, a major cause of vulvovaginal candidiasis.

• Design & Manufacturing
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• v.16 no.3
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• pp.28-33
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• 2022

The recent increasing application rate of advanced high-strength steel(AHSS) for automotive parts makes it difficult to ensure the durability of forming tools. Significant load and friction generated during the piercing process of AHSS increase the wear rate and the damage degree to dies. These harsh process conditions also yield product failures, such as dimensional inconsistency of pierced holes and insufficient quality of hole's sheared edge. This study analyzed the effect of punch wear on the sheared surface of pierced parts and the forming load during the piercing process. Wear-shaped punches showed approximately 20% higher piercing load than normal-shaped punches, and the rollover ratio of the sheared surface also increased. It is considered that the dull edge of wear-shaped punches does not penetrate directly into the material but shears after tensioning it in a piercing direction. In addition, wear-shaped punches experienced compressive load even after completing the piercing process during the down-stroke and tensile load during the up-stroke. This load variation is related to the smaller diameter piercing holes produced by wear-shaped punches compared to normal-shaped punches. Thus, we demonstrated the predictability of the wear level of dies through a comparative analysis of the piercing load pattern.

• Microbiology and Biotechnology Letters
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• v.51 no.1
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• pp.1-9
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• 2023

Arsenic (As), which is ubiquitous throughout the environment, represents a major environmental threat at higher concentration and poses a global public health concern in certain geographic areas. Most of the conventional arsenic remediation techniques that are currently in use have certain limitations. This situation necessitates a potential remediation strategy, and in this regard bioremediation technology is increasingly important. Being the oldest representativse of life on Earth, microbes have developed various strategies to cope with hostile environments containing different toxic metals or metalloids including As. Such conditions prompted the evolution of numerous genetic systems that have enabled many microbes to utilize this metalloid in their metabolic activities. Therefore, within a certain scope bacterial isolates could be helpful for sustainable management of As-contamination. Research interest in microbial As(III) oxidation has increased recently, as oxidation of As(III) to less hazardous As(V) is viewed as a strategy to ameliorate its adverse impact. In this review, the novelty of As(III) oxidation is highlighted and the implication of As(III)-oxidizing microbes in environmental management and their prospects are also discussed. Moreover, future exploitation of As(III)-oxidizing bacteria, as potential plant growth-promoting bacteria, may add agronomic importance to their widespread utilization in managing soil quality and yield output of major field crops, in addition to reducing As accumulation and toxicity in crops.

• Applied Chemistry for Engineering
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• v.34 no.4
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• pp.455-459
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• 2023

Three different optimization studies were conducted for the synthesis of MIL-100(Fe) and MIL-100(Fe)@COF using design of experiments. In the first study, the optimal concentration of precursors was determined using a mixture design method, and a modified molar ratio of 0.4155:0.2664:0.3182 was found to yield the highest crystallinity. In the second study, a central composite design was used to optimize the main factors of synthesis temperature and time with a synthesis temperature of 161℃ and a synthesis time of 12 hours. In the third study, a screening design method was used to determine the effect of five precursors on the formation of MIL-100(Fe)@COF, and the presence of characteristic peaks at 1552, 1483, and 1354 cm^-1 was found to be important for the existence of the COF structure. MIL-100(Fe)@COF synthesized with a modified molar ratio of 0.4831:0.4169:0.1 was predicted to exhibit optimal conditions.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.144-156
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• 2023

Smart agriculture is a rapidly growing field that seeks to optimize crop yields and reduce risk through the use of advanced technology. A key challenge in this field is the need to create a comprehensive smart farm system that can effectively monitor and control the growth environment of crops, particularly when cultivating new varieties. This is where fuzzy theory comes in, enabling the collection and analysis of external environmental factors to generate a rule-based system that considers the specific needs of each crop variety. By doing so, the system can easily set the optimal growth environment, reducing trial and error and the user's risk burden. This is in contrast to existing systems where parameters need to be changed for each breed and various factors considered. Additionally, the type of house used affects the environmental control factors for crops, making it necessary to adapt the system accordingly. While developing such a framework requires a significant investment of labour and time, the benefits are numerous and can lead to increased productivity and profitability in the field of smart agriculture. We developed an AI platform for optimal control of facility houses by integrating data from mushroom crops and environmental factors, and analysing the correlation between optimal control conditions and yield. Our experiments demonstrated significant performance improvement compared to the existing system.

• The Plant Pathology Journal
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• v.39 no.4
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• pp.319-334
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• 2023

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.283-283
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• 2022

Abiotic stresses such as high/low temperature, drought, salinity, and submergence directly or indirectly influence the physiological status and molecular mechanisms of rice which badly affect yield. Especially, the low temperature causes harmful influences in the overall process of rice growth such as uneven germination and the establishment of seedlings, which has become one of the main limiting factors affecting rice production in the world. It is of great significance to find the candidate genes controlling low-temperature tolerance during seed germination and study their functions for breeding new rice cultivars with immense low-temperature tolerance during seed germination. In this study, 120 lines of Cheongcheong/Nagdong double haploid population were used for quantitative trait locus analysis of low-temperature germinability. The results showed significant difference in germination under low different temperature conditions. In total, 4 QTLs were detected on chromosome 3, 6, and 8. A total of 41 genes were identified from all the 4 QTLs, among them, 25 genes were selected by gene function annotation and further screened through quantitative real time polymerase chain reaction. Based on gene function annotation and level of expression under low-temperature, our study suggested OsGPq3 gene as a candidate gene controlling viviparous germination, ABA and GA signaling under low-temperature. This study will provide a theoretical basis for marker-assisted breeding.