• Food Science and Biotechnology
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• v.18 no.1
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• pp.113-117
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• 2009

A method was developed using enhanced liquid chromatography coupled with electrospray ionization mass spectrometry for the analysis and quantitation of 2 main potato glycoalkaloids, $\alpha$-chaconine, and $\alpha$-solanine, without any pre-concentration or derivatisation steps. Calibration curves generated by this technique exhibited a linear dynamic range from 0.025 to $50{\mu}g/mL$ and from 0.05 to $50{\mu}g/mL$ for $\alpha$-chaconine and $\alpha$-solanine, respectively. Matrix effects were evaluated by comparing calibration curves measured in matrix-matched and solvent-based systems. Ion suppression due to matrix effects was weak and extraction recoveries of 88 to 114% were obtained in different sample matrices spiked with analyte concentrations ranging from 15 to $35{\mu}g/mL$. Potatoes that had been genetically modified to tolerate glufosinate contained the same glycoalkaloid levels as their non-transgenic counterpart. We suggest complementing compositional comparison assessment strategy by validating quantitative analytical methods for the toxic glycoalkaloids in potato plants.

• Physical Therapy Rehabilitation Science
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• v.6 no.4
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• pp.182-188
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• 2017

Objective: The purpose of this study was to compare ankle function between adults with and without Down syndrome (DS). Design: Cross-sectional study. Methods: Ten adults with DS and 18 without participated in this study and underwent manual muscle test (MMT), range of motion (ROM) assessment, star excursion balance test (SEBT), and functional movement screen (FMS). The tests were demonstrated to increase their accuracy and the actual measurements were assessed after one or two demonstrations. To minimize the standby time and fatigue, the travelled distance and measuring order were adjusted. To remove the influence of shoes on the measurements, the shoes were taken off and only socks were worn. Results: Dorsal and plantar flexion MMTs of both ankles were significantly weaker and plantar flexion ROM of both ankles were significantly lower in adults with DS compared with those without (p<0.05). However, dorsal flexion ROM of both ankles were not significantly different between them. There were significant differences in distances measured in all the directions (anterior, anterolateral, lateral, posterolateral, posterior, posteromedial, medial, and anteromedial directions) of SEBT (p<0.05). Significant differences were also demonstrated in the scores of hurdle step, inline lunge, shoulder mobility, and rotary stability among the seven items of FMS (p<0.05). Conclusions: To enhance the dynamic stability of adults with DS, it is necessary to improve ankle stability by strengthening the ankle dorsal and plantar flexors.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.843-853
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• 2021

The objectives of this paper were to simply estimate soil loss levels as caused by wind in reclaimed tidal flat land (RTFL) and the threshold wind velocity in the RTFL. For this experiment, RTFL located at Haenam Bay was selected and a total of 150 soil samples were collected at the Ap horizon from the five soil series. The particle distribution curves, including the limit of the non-erodible particle size (D > 0.84 mm) for each Ap horizon soil, show that the proportions of non-erodible particle sizes that exceeded 0.84 mm were 4.3% (Taehan, TH), 8.9% (Geangpo, GP), 0.5% (Bokchun, BC), 1.6% (Poseung, PS) and 1.4% (Junbook, JB), indicating that the amount of non-erodible soil particles increased with an increase in the sand content. The average monthly, daily and instantaneous wind velocities were higher than the threshold friction velocity (TFV) calculated according to the dynamic velocity (V_d) by Bagnold, while the average monthly wind velocity was lower than those of the TFV suggested by the revised wind erosion equation (RWEQ) and wind erosion prediction system (WEPS). The susceptible proportions of erodible soil particles from the Ap horizon soil samples from each soil series could be significantly influenced by the proportion of sand particles between 0.025 and 0.5 mm (or 0.84 mm) in diameter regardless of the threshold wind velocity. Thus, further investigations are needed to estimate more precisely soil erosion in RTFL, which shows various soil characteristics, as these estimations of soil loss in the five soil series were obtained only when considering wind velocities and soil textures.

• Journal of Environmental Health Sciences
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• v.45 no.5
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• pp.405-425
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• 2019

Objective: This study aimed to review the methodologies for evaluation of consumer spray products containing engineered nanomaterials (ENM), particularly focusing on inhalation exposure. Method: Literature on the evaluation methods for aerosolized ENM exposure from consumer spray products were collected through academic web searching. Common methodologies used in the literature, including research reports and academic articles, were also introduced. Results: The number of ENM-containing products have shown a considerable increase over recent years, from 54 in 2005 to 1,827 in 2018. Currently there is still discussion over the existing regulations with regard to product safety. Analysis of both ENM suspensions in the products and their aerosols is important for risk assessment. Comparison between the phases suggests how the size and concentration of particles change during the spray process. To analyze the ENM suspensions, dynamic light scattering, electron microscopy techniques, and inductively coupled plasma with mass spectrometry were used. In the aerosol monitoring, direct-reading instruments have been used to monitor the aerosols and conventional active sampling is used together to supplement the lack of real-time monitoring. There are also some models for estimating inhalation exposure. These models may be used to estimate mass exposure to nanomaterials contained in consumer products. Conclusion: Although there is no standardized method to evaluate ENM exposure from consumer products, many concerns about ENM have emerged. Every potential measure to reduce exposure to ENM from spray product use should be implemented through a precautionary recognition.

• Wind and Structures
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• v.14 no.6
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• pp.517-536
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• 2011

The assessment of wind-induced motion plays an important role in the development and design of the majority of today's structures that push the limits of engineering knowledge. A vital part of the design is the prediction of wind-induced tall building motion and the assessment of its effects on occupant comfort. Little of the research that has led to the development of the various international standards for occupant comfort criteria have considered the effects of the low-frequency motion on task performance and interference with building occupants' daily activities. It has only recently become more widely recognized that it is no longer reasonable to assume that the level of motion that a tall building undergoes in a windstorm will fall below an occupants' level of perception and little is known about how this motion perception could also impact on task performance. Experimental research was conducted to evaluate the performance of individuals engaged in a manual tracking task while subjected to low level vibration in the frequency range of 0.125 Hz-0.50 Hz. The investigations were carried out under narrow-band random vibration with accelerations ranging from 2 milli-g to 30 milli-g (where 1 milli-g = 0.0098 $m/s^2$) and included a control condition. The frequencies and accelerations simulated are representative of the level of motion expected to occur in a tall building (heights in the range of 100 m -350 m) once every few months to once every few years. Performance of the test subjects with and without vibration was determined for 15 separate test conditions and evaluated in terms of time taken to complete a task and accuracy per trial. Overall, the performance under the vibration conditions did not vary significantly from that of the control condition, nor was there a statistically significant degradation or improvement trend in performance ability as a function of increasing frequency or acceleration.

• Korean Medical Education Review
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• v.23 no.2
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• pp.80-89
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• 2021

Professional identity formation (PIF) is an essential concept in professional education. Many scholars have explored conceptual frameworks of PIF and conducted empirical studies to advance an understanding of the construct in medical education. Despite its importance, it is unclear what educational approaches and assessment practices are actually implemented in medical education settings. Therefore, we conducted a literature review of empirical studies reporting educational practices for medical learners' PIF. We searched the Web of Science database using keywords and chose 37 papers for analysis based on inclusion and exclusion criteria. Thematic analysis was conducted. Most empirical papers (92%) were from North America and Western Europe and used qualitative research methods, including mixed methods (99%). The papers reported the use of reflection activities and elective courses for specific purposes, such as art as an educational activity. Patient and healthcare experiences were also found to be a central theme in medical learners' PIF. Through an iterative analysis of the key themes that emerged from the PIF studies, we derived the following key concepts and implications: (1) the importance of creating informal and incidental learning environments, (2) ordinary yet authentic patient experiences, (3) a climate of psychosocial safety in a learning environment embracing individual learners' background and emotional development, and (4) the reconceptualization of PIF education and assessment. In conclusion, research on PIF should be diversified to include various cultural and social contexts. Theoretical frameworks should also be diversified and developed beyond Kegan's developmental framework to accommodate the nonlinear and dynamic nature of PIF.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.75-91
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• 2023

The technical parameters and imaging interpretation criteria of the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) using multiparametric MRI (mpMRI) are updated in PI-RADS v2.1. These changes have been an expected improvement for prostate cancer evaluation, although some issues remain unsolved, and new issues have been raised. In this review, a brief overview of PI-RADS v2.1 is and several critical points are discussed as follows: the need for more detailed protocols of mpMRI, lack of validation of the revised transition zone interpretation criteria, the need for clarification for the revised diffusion-weighted imaging and dynamic contrast-enhanced imaging criteria, anterior fibromuscular stroma and central zone assessment, assessment of background signal and tumor aggressiveness, changes in the structured report, the need for the parameters for imaging quality and performance control, and indications for expansion of the system to include other indications.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.55-64
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• 2017

In order to assess the spatial and dynamic changes in bamboo forests, this study used the national-level spatial data between 1980 and 2010 to extract spatial information of bamboo forests through GIS technology. The results showed that the distribution types were mainly expansion, normal, mixed, damage, and separation. In case of mixed bamboo forest in the Sacheon region, the expansion characteristics were: area 2.5 ha, velocity 0.08 ha/yr, and distance 1.1 m/yr. The Phyllostachys pubescens forest in the Geojae region showed the following characteristics: area 1.9 ha, velocity 0.06 ha/yr, and distance 0.9 m/yr with where along from valley to ridge. This approach could provide a valuable tool for decision-making and implementations such as the bamboo forest management plan, environmental impact assessment for a preventing the bamboo expansion, and sustainable managing the bamboo resources.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.