• Korean Journal of Pharmacognosy
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• v.40 no.4
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• pp.408-414
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• 2009

This study was performed to investigate the anti-hyperglycemia effects of the Triticum aestivum wheat sprout (TAWS) water extracts in the diabetic mice. Diabetic experimental model was established by intraperitoneal injection of streptozotocin into male Balb/c mice. Mice were divided into five groups: normal (CON), diabetic control (DM), and three experimental groups (DM-100, diabetes with TAWS extracts 100mg/kg; DM-50, diabetes with TAWS extracts 50 mg/kg; DM-25, diabetes with TAWS extracts 25 mg/kg). TAWS extracts were administered orally in diabetic mice. Body weight, food intake, and blood glucose levels were recorded for 12 days and blood insulin levels were measured at the day 12. Oral administration of TAWS extracts reduced slightly food intake and induced a little body weight gain in DM-100 groups. The blood level of glucose was decreased in the dose-dependent manner; 55% in the DM-100 group and 39.7% in the DM-50 group. The blood level of insulin also was improved 10 folds in the DM-100 group and 3.6 folds in the DM-50 group compared to the DM group. The contents of total phenolic compounds and total flavonoids in 1 g dry mass of TAWS extracts were 6.6 mg of tannic acid equivalents and 1.0 mg of 8-hydroquinolline equivalents, respectively. In addition, the antioxidant and DPPH radical scavenging activity of TAWS extracts were 1.2 mM and 1.8 mM ascorbic acid equivalents, respectively. These results suggest that TAWS water extracts could contribute to attenuate clinical symptoms of diabetes mellitus.

• Journal of Preventive Medicine and Public Health
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• v.56 no.5
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• pp.407-412
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• 2023

Objectives: Work stress is associated with non-communicable diseases, increased healthcare costs, and decreased work productivity among employees in the information technology sector. There is a need for regular work-stress screening among employees using valid and reliable tools. The Tool to Assess and Classify Work Stress (TAWS-16) was developed to overcome limitations in existing stress assessment tools in India. This study aimed to test the reliability of TAWS-16 in a sample of managerial-supervisory employees. Methods: This observational reliability study included data from 62 employees. Test-retest and inter-method reliability were investigated using a TAWS-16 web application and interview by telephone, respectively. Kappa values and intra-class correlation coefficients were calculated. Internal consistency was assessed through Cronbach's alpha. Results: For both test-retest and inter-method reliability, the agreement for both work-related factors and symptoms suggestive of work stress exceeded 80%, and all kappa values were 0.40 or higher. Cronbach's alpha for test-retest and inter-method reliability was 0.983 and 0.941, respectively. Conclusions: TAWS-16 demonstrated acceptable reliability. It measured stressors, coping abilities, and psychosomatic symptoms associated with work stress. We recommend using TAWS-16 to holistically identify work stress among employees during periodical health check-ups in India.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.30 no.2
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• pp.34-43
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• 2022

In Efforts to prevent CFIT accidents so far, have been emphasizing various education measures to minimize the occurrence of human errors, as well as enforcement measures. However, current engineering measures remain in a system (TAWS) that gives warnings before colliding with ground or obstacles, and even actual automatic avoidance maneuvers are not implemented, which has limitations that cannot prevent accidents caused by human error. Currently, various attempts are being made to apply machine learning-based artificial intelligence agent technologies to the aviation safety field. In this paper, we propose a deep reinforcement learning-based artificial intelligence agent that can recognize CFIT situations and control aircraft to avoid them in the simulation environment. It also describes the composition of the learning environment, process, and results, and finally the experimental results using the learned agent. In the future, if the results of this study are expanded to learn the horizontal and vertical terrain radar detection information and camera image information of radar in addition to the terrain database, it is expected that it will become an agent capable of performing more robust CFIT autonomous avoidance.