• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.859-866
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• 2021

Agricultural Photovoltaic power generation is a new model that installs solar power generation facilities on top of farmland. Through this, it is possible to increase farm household income by producing crops and electricity at the same time. Recently, various attempts have been made to utilize agricultural solar power generation. Agricultural photovoltaic power generation has a disadvantage in that maintenance is relatively difficult because it is installed on a relatively high structure unlike conventional photovoltaic power generation. To solve these problems, intelligent and efficient operation and diagnostic functions are required. In this paper, we discuss the design and implementation of a prediction and diagnosis system to collect and store the power output of agricultural solar power generation facilities and implement an intelligent prediction model. The proposed system predicts the amount of power generation based on the amount of solar power generation and environmental sensor data, determines whether there is an abnormality in the facility, calculates the aging degree of the facility and provides it to the user.

• Safety and Health at Work
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• v.9 no.4
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• pp.381-387
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• 2018

Background: Physician behaviors that undermine a culture of safety have gained increasing attention as health-care organizations strive to create a culture of safety and reduce medical errors. We developed, implemented, and assessed a course to teach physicians skills regarding effective coping and interpersonal communication skills and present our results regarding outcomes. Methods: We examined a professional development program specifically designed to address unprofessional or distressed behaviors of physicians, and we evaluated the impact on burnout, quality of life, and emotional flooding scores of the physicians. Assessments of burnout, quality of life, and emotional flooding were assessed preintervention and postintervention. Results: Results demonstrated statistically significant reductions over time in physicians' emotional flooding and emotional exhaustion (EE). Specifically, using a Wilcoxon Signed-Rank test, results revealed that flooding scores at follow-up were statistically significantly lower than at baseline, V = 590, p < 0.05, and EE and personal accomplishment distributions were found to significantly deviate from normal as indicated by Shapiroe-Wilks tests (p < 0.05). A Wilcoxon signed-rank test indicated that EE scores were significantly higher at baseline compared to follow-up 1, V = 285, p < 0.05. Conclusion: We conclude that the physician participants who enrolled in the educational skills training program improved scores on emotional flooding and EE and that this may be indicative of improved skills related to their experiences and learning in the program. These improved skills in physicians may have a positive impact on the overall culture of safety in the health system setting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.1957-1980
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• 2021

The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.59-60
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• 2013

Infobutton is an important concept from long time in use and much has been done with respect to its standardization and context supplementation. The concept is to create contextual links to information resources from within the information systems usually health information systems. The need which has been realized by the authors of this paper is the augmentation of Infobuttons from the level of only information links to the level of knowledge links. The authors proposed the concept of knowledge links named as "Knowledgebuttons" which complements the concept Infobuttons. It adds further capabilities of getting knowledge to the users instead of just connectivity to information resources. The better representation of the information retrieved with Infobuttons is the first and foundation step to achieve the goal of getting knowledge. This paper discusses about the concept and applicability of Knowledgebuttons in health information systems. It is envisioned that this concept will add to the overall quality of patient care. Both physicians and patients can benefit from this technique as per their needs. Physicians can help in patient diagnosis and treatment critical decisions while patients can educate them to know more about their health conditions by studying the right knowledge at right time. Knowledgebuttons are able to create a true learning environment for the users while using health information systems.

• CELLMED
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• v.11 no.1
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• pp.2.1-2.8
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• 2021

The clinical condition Amnesia causes difficulty in learning new information and the inability to recall past events. It is primarily concerned with recent memory loss. Majoon-e-Nisyan (MJN) is a polyherbal Unani formulation, present in a semi-solid form. It is widely used potent drug of the Unani System of Medicine (USM) for treating Nisyan (amnesia). In the present study polyherbal Unani formulation, MJN has been studied for its quality control and acute toxicity. Standardization (quality control) of drugs deals with drug identity, drug quality and purity determination. Standardization of MJN had been done as per the Unani pharmacopoeial parameters approved by World Health Organization (WHO) - Pharmacognostical parameters, Physico-chemical parameters, high-performance thin-layer chromatography (HPTLC), microbial load, aflatoxin, and heavy metals. Solvents and chemicals used in the study were of analytical grade and used instrument were calibrated. By conducting an acute oral toxicity study in rats, the safety of MJN was assessed. The limit test method of OECD guideline 425 was followed in the study. Results of standardization and standard operating procedures (SOPs) for preparation of MJN may serve as the standard reference in the future. The data generated in the study for the quality control of MJN proved the quality of formulation and shows that MJN is not toxic in rats following acute dosing up to 2000 mg/kg bw. The data obtained in the paper for MJN may be used as a standard guideline for preparation of the formulation which can save time, cost, and resources for future research endeavours.

• International Journal of Knowledge Content Development & Technology
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• v.11 no.4
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• pp.7-34
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• 2021

The purpose of the study was to explore the uses and gratification of social media among first-year student groups at a satellite campus of a public university in Ghana. The study employed a descriptive survey design. The study involved all 1061 first-year university students in six academic departments of the College. A total of 680 (64%) participants returned validly completed copies of the questionnaire. Descriptive statistics and thematic analysis were employed for data analysis. The findings indicate that WhatsApp was the most popular application for social media groups, while a need for information-sharing, peer-tutoring and learning, and finding and keeping friends were the primary motivations for joining social media groups. First-year students are involved mainly in reactive activities, as most engage when solving an academic assignment through group discussions. Though challenges persist, such as posting of unwanted images, inadequate participation, and ineffective and irrelevant communication, most are willing to continue their social media groups' membership in the long term. This study provides valuable insight into transitioning students' lived experiences on social media from the group perspective. These insights are valuable conceptually and practically to academic counsellors, librarians and student affairs officers who are expected to provide on-going education on (social) media literacy to first-year students to enhance the adjustment process. The study is the first of its kind in Ghana that investigates social media group participants' exit intentions.

• The Korean Journal of Food And Nutrition
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• v.35 no.1
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• pp.7-15
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• 2022

Maca roots (Lepidium meyenii) are an important medicinal herb that have long been used by the Andes-indigenous peoples and South Americans. In Korea, recently, it has attracted attention as a health food material because of nutritional values and physiological activities. The purpose of this study was to investigate the industrial applicability of maca (red and golden varieties; R&G) as immunostimulating materials. In the macrophage stimulating assay using RAW 264.7 cells at 125~500 ㎍/mL of non-cytotoxicity doses, G-HW showed the most potent production of TNF-α, IL-6 and nitric oxide compared to red maca or the other extracts. The general component analysis results showed that all extracts comprised more than 90% neutral sugars with small amounts of uronic acid and protein. Meanwhile, component sugar analysis showed the difference in the content of uronic acids of cold- and hot-water extract. Additionally, the further fractionation of G-HW into crude polysaccharide (G-CP) greatly enhanced the macrophage stimulating activity, and G-CP contained macromolecules over 144 kDa, comprised mainly of glucose and galacturonic acid (51.0 and 34.9%). In conclusion, crude polysaccharide from maca showed industrial applicability as immunostimulating material, and especially golden maca showed higher macrophage stimulating activity than red maca.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.8
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• pp.19-27
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• 2022

Graduate well-being is foundational to academic success, and they are becoming more and more vulnerable. This is as they suffer from mental health challenges like anxiety and depression at rates six times higher than the general population. When the nature of their educational experience changes, such as when they had to stay in their homes during the COVID-19 pandemic, the stress on their mental health increases. The number of cases of emotional wellness among university students is considered a public health problem, but these young people often do not seek appropriate treatment. This study, therefore, aims to identify the influence of health behavior factors on graduate emotional wellness. This study used a questionnaire with a cross-sectional survey design. Questionnaires were distributed online to graduates from selected Private and Public Higher Education Institutions in Malaysia. The Partial Least Square Equation Model (PLS-SEM) was used to analyze the results of the study. Overall findings indicate that the health behavior factors have a significant influence on graduate emotional wellness. The findings from this study will benefit the management, academics, counselors, and other entities, including the Students' Representative Council, in identifying ways to improve services and upgrade the necessary facilities to enhance the graduate's emotional wellness.

• Clinics in Shoulder and Elbow
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• v.25 no.2
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• pp.145-153
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• 2022

Background: While online orthopedic resources are becoming an increasingly popular avenue for patient education, videos on YouTube are not subject to peer review. The purpose of this cross-sectional study was to evaluate the quality of YouTube videos for patient education in ulnar collateral ligament (UCL) injuries of the elbow. Methods: A search of keywords for UCL injury was conducted through the YouTube search engine. Each video was categorized by source and content. Video quality, reliability, and accuracy were assessed by two independent raters using five metrics: (1) Journal of American Medical Association (JAMA) benchmark criteria (range 0-4) for video reliability; (2) modified DISCERN score (range 1-5) for video reliability; (3) Global Quality Score (GQS; range 1-5) for video quality; (4) ulnar collateral ligament-specific score (UCL-SS; range 0-16), a novel score for comprehensiveness of health information presented; and (5) accuracy score (AS; range 1-3) for accuracy. Results: Video content was comprised predominantly of disease-specific information (52%) and surgical technique (33%). The most common video sources were physician (42%) and commercial (23%). The mean JAMA score, modified DISCERN score, GQS, UCL-SS, and AS were 1.8, 2.4, 1.9, 5.3, and 2.7 respectively. Conclusions: Overall, YouTube is not a reliable or high-quality source for patients seeking information regarding UCL injuries, especially with videos uploaded by non-physician sources. The multiplicity of low quality, low reliability, and irrelevant videos can create a cumbersome and even inaccurate learning experience for patients.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.63-73
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• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.