• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.406-413
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• 2019

The purpose of this research was to analyze the effect of different sowing times on the flowering and maturing of major soybean cultivars by varying day length and temperature in the central plain region. The average of growth period and cumulative temperature in five test cultivars by sowing times were 121 days and 2,972℃ on June 1, respectively and gradually decreased to 85 days, 2,042℃, respectively on July 20. Analysis of the flowering response according to the sowing times showed that flowering was greatly influenced by the decrease of photoperiod until the sowing on July 10, and the minimum number of days for flowering were 27 days, 36 days, respectively in early and mid-rate maturing type in the central plain region. Daepung 2 is classified to the same ecotype with Daewonkong, the total number of growing days was not different between two cultivars, but ripening period (R2-R6) was longer by 5 days and yield was higher by 11% in Daepung 2. The maturity rate was also high and safe enough to maintain more than 90% through the entire sowing times. This ecological characteristic can be usefully applied as a section index for breeding environmental stress resistant and high yielding soybean varieties. The yield of 4 domestic cultivars (except TI196944) sowing on July 20 were 85~92% levels compared to sowing on June 20.

• Journal of the Korean Society for Precision Engineering
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• v.3 no.1
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• pp.40-49
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• 1986

Crack, craze and void are common defects which may be found in the bulk of polymeric materials such as either themoplastics or thermosets. The healing phenomena, autohesion, of these defects are known to be a intrinsic material property of various polymeric materials. However, only a few experimental and theoretical investigations on crack, void and craze healing phenomena for various polymeric materials have been reported up to date [1, 2, 3]. This may be partly due to the complications of healing processes and lacking of appropriate theoretical developments. Recently, some investigators have been urged to study the healing phenomena of various polymenic materials since the significance of the use of polymer based alloys or composites has been raised in terms of specific strength and energy saving. In the earlier published reports [1, 2, 3, 4], the crack and void healing velocity, healing toughness and some other healing mechanical and physical properties were measured experimentally and compared with predicted values by utilizing a simple model such as the reptation model under some resonable assumptions. It seems, however, that the general acceptance of the proposed modeling analyses is yet open question. The crack healing processes seem to be complicate and highly dependent on the state of virgin material in terms of mechanical and physical properties. Furthermore, it is also strongly dependent on the histories of crack, craze and void development including fracture suface morphology, the shape of void and the degree of disentanglement of fibril in the craze. The rate of crack healing may be a function of environmental factors such as healing temperature, time and pressure which gives different contact configurations between two separated surfaces. It seems to be reasonable to assume that the crack healing processes may be divided in several distinguished steps like stress relaxation with molecular chain arrangement, surface contact (wetting), inter- diffusion process and com;oete healing (to obtain the original strength). In this context, it is likely that we no longer have to accept the limitation of cumulative damage theories and fatigue life if it is probable to remove the defects such as crack, craze and void and to restore the original strength of polymers or polymer based compowites by suitable choice of healing histories and methods. In this paper, we wish to present a very simple and intuitive theoretical model for the prediction of healed fracture toughness of cracked or defective polymeric components. The central idea of this investigation, thus, may be the modeling of behavior of chain molecules under healing conditions including the effects of chain scission on the healing processes. The validity of this proposed model will be studied by making comparisons between theoretically predicted values and experimentally determined results in near future and will be reported elsewhere.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5271-5276
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• 2014

Mitogen-activated protein kinase/extracellular signal-regulated kinase kinase kinase 3 (MEKK3) is an important protein kinase and a member of the MAPK family, which regulates cellular responses to environmental stress and serves as key integration points along the signal transduction cascade that not only link diverse extracellular stimuli to subsequent signaling molecules but also amplify the initiating signals to ultimately activate effector molecules and induce cell proliferation, differentiation and survival. To explore the relationship between MEKK3 and cell apoptosis, clinicopathology and prognosis, we characterize the expression of MEKK3 and survivin in cervical cancer. MEKK3 and survivin expression was measured by RT-PCR and Western blotting of fresh surgical resections from 30 cases of cervical cancer and 25 cases of chronic cervicitis. Protein expression was detected by tissue microarray and immunochemistry (En Vision) in 107 cases of cervical cancer, 86 cases of cervical intraepithelial neoplasia (CIN), and 35 cases of chronic cervicitis. Expression patterns were analyzed for their association with clinicopathological factors and prognosis in cervical cancer. Expression of MEKK3 and survivin mRNA was significantly higher in cervical cancer than in the controls (p<0.05). MEKK3 and survivin expression differed significantly between cervical carcinoma, CIN, and cervicitis (p<0.05) and correlated with clinical stage, infiltration depth, and lymph node metastasis (p<0.05). MEKK3 expression was positively correlated with survivin (p<0.05). Kaplan-Meier survival analysis showed that MEKK3 and survivin expression, lymph node metastasis, depth of invasion, and FIGO stage reduce cumulative survival. Cox multivariate regression analysis showed that MEKK3, survivin, and clinical staging are independent prognostic factors in cervical cancer (p<0.05). Expression of MEKK3 and survivin are significantly increased in cervical cancer, their overexpression participating in the occurrence and development of cervical cancer, with protein expression and clinical staging acting as independent prognostic factors for patients with cervical cancer.

• Journal of the Ergonomics Society of Korea
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• v.36 no.3
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• pp.183-196
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• 2017

Objective: The purpose of this research was to assess the agreement between job physical risk factor analysis by ergonomists using ergonomic methods and physical examinations made by occupational physicians on the presence of musculoskeletal disorders of the upper extremities. Background: Ergonomics is the systematic application of principles concerned with the design of devices and working conditions for enhancing human capabilities and optimizing working and living conditions. Proper ergonomic design is necessary to prevent injuries and physical and emotional stress. The major types of ergonomic injuries and incidents are cumulative trauma disorders (CTDs), acute strains, sprains, and system failures. Minimization of use of excessive force and awkward postures can help to prevent such injuries Method: Initial data were collected as part of a larger study by the University of Utah Ergonomics and Safety program field data collection teams and medical data collection teams from the Rocky Mountain Center for Occupational and Environmental Health (RMCOEH). Subjects included 173 male and female workers, 83 at Beehive Clothing (a clothing plant), 74 at Autoliv (a plant making air bags for vehicles), and 16 at Deseret Meat (a meat-processing plant). Posture and effort levels were analyzed using a software program developed at the University of Utah (Utah Ergonomic Analysis Tool). The Ergonomic Epicondylitis Model (EEM) was developed to assess the risk of epicondylitis from observable job physical factors. The model considers five job risk factors: (1) intensity of exertion, (2) forearm rotation, (3) wrist posture, (4) elbow compression, and (5) speed of work. Qualitative ratings of these physical factors were determined during video analysis. Personal variables were also investigated to study their relationship with epicondylitis. Logistic regression models were used to determine the association between risk factors and symptoms of epicondyle pain. Results: Results of this study indicate that gender, smoking status, and BMI do have an effect on the risk of epicondylitis but there is not a statistically significant relationship between EEM and epicondylitis. Conclusion: This research studied the relationship between an Ergonomic Epicondylitis Model (EEM) and the occurrence of epicondylitis. The model was not predictive for epicondylitis. However, it is clear that epicondylitis was associated with some individual risk factors such as smoking status, gender, and BMI. Based on the results, future research may discover risk factors that seem to increase the risk of epicondylitis. Application: Although this research used a combination of questionnaire, ergonomic job analysis, and medical job analysis to specifically verify risk factors related to epicondylitis, there are limitations. This research did not have a very large sample size because only 173 subjects were available for this study. Also, it was conducted in only 3 facilities, a plant making air bags for vehicles, a meat-processing plant, and a clothing plant in Utah. If working conditions in other kinds of facilities are considered, results may improve. Therefore, future research should perform analysis with additional subjects in different kinds of facilities. Repetition and duration of a task were not considered as risk factors in this research. These two factors could be associated with epicondylitis so it could be important to include these factors in future research. Psychosocial data and workplace conditions (e.g., low temperature) were also noted during data collection, and could be used to further study the prevalence of epicondylitis. Univariate analysis methods could be used for each variable of EEM. This research was performed using multivariate analysis. Therefore, it was difficult to recognize the different effect of each variable. Basically, the difference between univariate and multivariate analysis is that univariate analysis deals with one predictor variable at a time, whereas multivariate analysis deals with multiple predictor variables combined in a predetermined manner. The univariate analysis could show how each variable is associated with epicondyle pain. This may allow more appropriate weighting factors to be determined and therefore improve the performance of the EEM.