The objective of this experiment was to investigate the effect of drip irrigation volume on tomatoes (Solanum lycopersicum L.) grown in a greenhouse using perlite medium. Plants were treated by three different irrigation treatment I0, I25, and I50 (where irrigation volume of I25 and I50 was 25% and 50% higher than I0, having limited or no leaching). Growth characteristics of plants, yield and water use efficiency were measured. The result showed that plant height, leaf length and leaf width were lowest in the I0 treated plants. However, these parameters were not statistically significant differences between the plants that were grown in the I25 and I50 treatment. Soluble solids content, acidity and dry matter of 111th, 132nd, and 143rd days harvested tomato were higher in the plants irrigated with lowest volume (I0) than the higher volume (I25 or I50). In addition, water content was lower in the 111th and 132nd days of harvested tomatoes from the I0 treatment. The number of big-size tomatoes (>180 g) was significantly higher in the I25 irrigated plants. There was no significant difference in the total number of harvested fruits among the treatments. The average fruit weight and total yield of harvested tomatoes were lowest in the I0 treated plants. The water consumption of tomato was not significantly different amongst the treatments but water use efficiency was lowest in the I0 treatment. Principal component analysis revealed that total soluble solid and acidity of tomato showed a positive correlation between each other. These results suggest that I25 was the optimum irrigation treatment for tomato based on its measured growth characteristics, yield and water use efficiency.

This investigation aimed to assess the appetite response changes of olive flounder to starving and re-feeding conditions. Three different feeding groups (2 weeks feeding, fed; 2 weeks starving, starved; and 1 week starving and 1 week feeding, re-fed) were established to examine the changes in appetite-related genes for each group. The weight gain of the fish was highest for the fed group and lowest for the starving group. Based on the daily feed intake (DFI) and cumulative feed intake (CFI), overall food intake was found to increase in the re-fed group more than in the fed group from week 1 to week 2 of the experiment. Hypocretin neuropeptide precursor (HCRT) and galanin receptor 1 (GAL-R1) mRNA expression in the brain of olive flounder were decreased in the starved group. Corticotropin-releasing hormone (CRH) was decreased in all experimental groups, except for the fed group. However, overall leptin concentrations in the plasma did not change across groups. Considering the differences between this study and previous studies on starving and feeding, various factors (except the production and expression mechanisms of appetite-related factors in response to starving) are likely acting on the appetite responses of the fish. In this study, a 1-week re-feeding period induced substantial effects on appetite response when compared to a 2-week feeding period. These findings show that even if re-feeding is performed after starving, the unbalance caused by the re-feeding can affect various physiological changes in fish by feed intake efficiency.

Periodontitis and periimplantitis are caused as a result of dental biofilm formation. This biofilm is composed of multiple species of pathogens. Therefore, controlling biofilm formation is critical for disease prevention. To inhibit biofilm formation, sugars can be used to interrupt lectin-involving interactions between bacteria or between bacteria and a host. In this study, we evaluated the effect of D-Arabinose on biofilm formation of putative periodontal pathogens as well as the quorum sensing activity and whole protein profiles of the pathogens. Crystal violet staining, confocal laser scanning microscopy, and scanning electron microscopy revealed that D-Arabinose inhibited biofilm formation of Porphyromonas gingivalis , Fusobacterium nucleatum , and Tannerella forsythia . D-Arabinose also significantly inhibited the activity of autoinducer 2 of F. nucleatum and the expression of representative bacterial virulence genes. Furthermore, D-Arabinose treatment altered the expression of some bacterial proteins. These results demonstrate that D-Arabinose can be used as an antibiofilm agent for the prevention of periodontal infections.

In recent years, importance of blockchain systems has been grown after success of bitcoin. Distributed consensus algorithm is used to achieve an agreement, which means the same information is recorded in all nodes participating in blockchain network. Various algorithms were suggested to resolve blockchain trilemma, which refers conflict between decentralization, scalability, security. An algorithm based on Byzantine Agreement among Decentralized Agents (BADA) were designed for the same manner, and it used limited committee that enables an efficient consensus among considerable number of nodes. In addition, election of committee based on Proof-of-Nonce guarantees decentralization and security. In spite of such prominence, application of BADA in actual blockchain system requires further researches about performance and essential features affecting on the performance. However, performance assessment committed in real systems takes a long time and costs a great deal of budget. Based on this motivation, we designed and implemented a simulator for measuring performance of BADA. Specifically, we defined a simulation framework including three components named simulator Command Line Interface, transaction generator, BADA nodes. Furthermore, we carried out response surface analysis for revealing latent relationship between performance measure and design parameters. By using obtained response surface models, we could find an optimal configuration of design parameters for achieving a given desirable performance level.

Recently, a lots of studies have been conducted to imitate real meat with the factors including texture, aroma, taste, and color of meat analogues. Since the color component of meat is diverse and complex, as well as large of degree of change according to cooking, the color imitating has been still challenging. In this study, we selected up a representative meat analogue (tteok-galbi), tracked the color change at three making points as steps of batter, steaming, and cooking. In order to realize the color at these points, three plant-based natural pigments including prickly pear cactus, sweet pumpkin, and mugwort were selected, and the color with optimized ratio was applied to the tteok-galbi. As a result, it was found that 1:3:1 in the batter, 2:1:2 in steaming, and 2:1:2 in cooking were optimal ratios. The results in this study were significant in that it could apply the colors to meat analogues according to the combination of natural pigments derived from plants.

Various elements of Fabrication (FAB), mass production of existing products, new product development and process improvement evaluation might increase the complexity of production process when products are produced at the same time. As a result, complex production operation makes it difficult to predict production capacity of facilities. In this environment, production forecasting is the basic information used for production plan, preventive maintenance, yield management, and new product development. In this paper, we tried to develop a multiple linear regression analysis model in order to improve the existing production capacity forecasting method, which is to estimate production capacity by using a simple trend analysis during short time periods. Specifically, we defined overall equipment effectiveness of facility as a performance measure to represent production capacity. Then, we considered the production capacities of interrelated facilities in the FAB production process during past several weeks as independent regression variables in order to reflect the impact of facility maintenance cycles and production sequences. By applying variable selection methods and selecting only some significant variables, we developed a multiple linear regression forecasting model. Through a numerical experiment, we showed the superiority of the proposed method by obtaining the mean residual error of 3.98%, and improving the previous one by 7.9%.

Process mining is an analytical technique aimed at obtaining useful information about a process by extracting a process model from events log. However, most existing process models are deterministic because they do not include stochastic elements such as the occurrence probabilities or execution times of activities. Therefore, available information is limited, resulting in the limitations on analyzing and understanding the process. Furthermore, it is also important to develop an efficient methodology to discover the process model. Although genetic process mining algorithm is one of the methods that can handle data with noises, it has a limitation of large computation time when it is applied to data with large capacity. To resolve these issues, in this paper, we define a stochastic process tree and propose a tabu search-genetic process mining (TS-GPM) algorithm for a stochastic process tree. Specifically, we define a two-dimensional array as a chromosome to represent a stochastic process tree, fitness function, a procedure for generating stochastic process tree and a model trace as a string of activities generated from the process tree. Furthermore, by storing and comparing model traces with low fitness values in the tabu list, we can prevent duplicated searches for process trees with low fitness value being performed. In order to verify the performance of the proposed algorithm, we performed a numerical experiment by using two kinds of event log data used in the previous research. The results showed that the suggested TS-GPM algorithm outperformed the GPM algorithm in terms of fitness and computation time.