• CELLMED
• /
• v.6 no.1
• /
• pp.6.1-6.6
• /
• 2016

Polycystic Ovarian Syndrome (PCOS) has now become more common in occurrence in women of reproductive age, particularly in urban and semi-urban population in India. So there is a need to investigate this phenomenon taking into consideration various aspects including possible treatment method to ameliorate/eradicate this syndrome, which has far reaching socio-economic impact and consequences, in view of infertility and irregular menstrual cycles frequently associated with this syndrome. Homeopathy is a branch of traditional alternative medicine which is gaining popularity in India and some other developing countries, as also in some of the developed countries in Europe. With this background scenario, we have made an attempt to treat cases of confirmed PCOS and tried to compare the relative efficacy of two potentized homeopathic drugs, namely, Lycopodium clavatum (Lyco) and Calcarea carbonica (Calc), most frequently used by homeopathic practitioners, selecting different potencies of the drugs, depending on condition/guiding symptom(s) of the patients. While the main focus was pointed on total/partial removal of cysts, data pertaining to different PCOS associated symptoms were also compared for the sake of learning if the two drugs had differential effects on these symptoms also. The study parameters in this investigation included: regularity/irregularity of menstrual cycle, presence/absence of acne, hirsutism, male type alopecia, acanthosis nigricans, body/mass index (BMI) and waist-hip ratio. Overall results provided clear evidences that both these homeopathic drugs had great ameliorating effects on PCOS, although each drug had a little different effect in respect of the individual parameters of this study.

• Journal of Environmental Health Sciences
• /
• v.48 no.2
• /
• pp.96-105
• /
• 2022

Background: Pyrethroid insecticides account for more than 30% of the global insecticide market and are frequently used in agricultural settings and residential and public pest control among the general population. While several animal studies have suggested that exposure to pyrethroids can alter glucose homeostasis, there is only limited evidence of the association between environmental pyrethroid exposure and diabetes in humans. Objectives: This study aimed to report environmental 3-phenoxybenzoic acid (3-PBA) concentrations in urine and evaluate its association with the risk of diabetes in Korean adults. Methods: We analyzed data from the Korean National Environmental Health Survey (KoNEHS) Cycle 2 (2012~2014) and Cycle 3 (2015~2017). A total of 10,123 participants aged ≥19 years were included. Multiple logistic regressions were used to calculate the odds ratios (ORs) for diabetes according to log-transformed urinary 3-PBA levels. We also evaluated age, sex, education, monthly income, marital status, alcohol drinking, physical activity, urinary cotinine, body mass index, and sampling season as potential effect modifiers of these associations. Results: After adjusting for all the covariates, we found significant dose-response relationships between urinary 3-PBA as quartile and the prevalence of diabetes in pooled data of KoNEHS Cycles 2 and 3. In subgroup analyses, the adverse effects of pyrethroid exposure on diabetes were significantly stronger among those aged 19~39 years (p-interaction<0.001) and those who consumed high levels of cotinine (p-interaction=0.020). Conclusions: Our findings highlight the potential diabetes risk of environmental exposure to pyrethroids and should be confirmed in large prospective studies in different populations in the future.

• International Journal of Computer Science & Network Security
• /
• v.22 no.3
• /
• pp.37-44
• /
• 2022

Plug-in Hybrid electric vehicles (PHEV) show great potential to reduce gas emission, improve fuel efficiency and offer more driving range flexibility. Moreover, PHEV help to preserve the eco-system, climate changes and reduce the high demand for fossil fuels. To address this; some basic components and energy resources have been used, such as batteries and proton exchange membrane (PEM) fuel cells (FCs). However, the FC remains unsatisfactory in terms of power density and response. In light of the above, an electric storage system (ESS) seems to be a promising solution to resolve this issue, especially when it comes to the transient phase. In addition to the FC, a storage system made-up of an ultra-battery UB is proposed within this paper. The association of the FC and the UB lead to the so-called Fuel Cell Battery Electric Vehicle (FCBEV). The energy consumption model of a FCBEV has been built considering the power losses of the fuel cell, electric motor, the state of charge (SOC) of the battery, and brakes. To do so, the implementing a reinforcement-learning energy management strategy (EMS) has been carried out and the fuel cell efficiency has been optimized while minimizing the hydrogen fuel consummation per 100km. Within this paper the adopted approach over numerous driving cycles of the FCBEV has shown promising results.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.17 no.6
• /
• pp.25-39
• /
• 2018

The purpose of this study is to develop an efficient incident detection algorithm by applying machine learning, which is being widely used in the transport sector. As a first step, network of the target site was constructed with micro-simulation model. Secondly, data has been collected under various incident scenarios produced with combination of variables that are expected to affect the incident situation. And, detection results from both McMaster algorithm, a well known incident detection algorithm, and the Naive Bayes algorithm, developed in this study, were compared. As a result of comparison, Naive Bayes algorithm showed less negative effect and better detect rate (DR) than the McMaster algorithm. However, as DR increases, so did false alarm rate (FAR). Also, while McMaster algorithm detected in four cycles, Naive Bayes algorithm determine the situation with just one cycle, which increases DR but also seems to have increased FAR. Consequently it has been identified that the Naive Bayes algorithm has a great potential in traffic incident detection.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1757-1766
• /
• 2021

The green coverage ratio is the ratio of the land area to green coverage area, and it is used as a practical urban greening index. The green coverage ratio is calculated based on the land cover map, but low spatial resolution and inconsistent production cycle of land cover map make it difficult to calculate the correct green coverage area and analyze the precise green coverage. Therefore, this study proposes a new method to calculate green coverage area using aerial images and deep neural networks. Green coverage ratio can be quickly calculated using manned aerial images acquired by local governments, but precise analysis is difficult because components of image such as acquisition date, resolution, and sensors cannot be selected and modified. This limitation can be supplemented by using an unmanned aerial vehicle that can mount various sensors and acquire high-resolution images due to low-altitude flight. In this study, we proposed a method to calculate green coverage ratio from manned or unmanned aerial images, and experimentally verified the proposed method. Aerial images enable precise analysis by high resolution and relatively constant cycles, and deep learning can automatically detect green coverage area in aerial images. Local governments acquire manned aerial images for various purposes every year and we can utilize them to calculate green coverage ratio quickly. However, acquired manned aerial images may be difficult to accurately analyze because details such as acquisition date, resolution, and sensors cannot be selected. These limitations can be supplemented by using unmanned aerial vehicles that can mount various sensors and acquire high-resolution images due to low-altitude flight. Accordingly, the green coverage ratio was calculated from the two aerial images, and as a result, it could be calculated with high accuracy from all green types. However, the green coverage ratio calculated from manned aerial images had limitations in complex environments. The unmanned aerial images used to compensate for this were able to calculate a high accuracy of green coverage ratio even in complex environments, and more precise green area detection was possible through additional band images. In the future, it is expected that the rust rate can be calculated effectively by using the newly acquired unmanned aerial imagery supplementary to the existing manned aerial imagery.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.5
• /
• pp.1-18
• /
• 2023

With the recent developments in big data and deep learning, a variety of traffic information is collected widely and used for traffic operations. In particular, long short-term memory (LSTM) is used in the field of traffic information prediction with time series characteristics. Since trends, seasons, and cycles differ due to the nature of time series data input for an LSTM, a trial-and-error method based on characteristics of the data is essential for prediction models based on time series data in order to find hyperparameters. If a methodology is established to find suitable hyperparameters, it is possible to reduce the time spent in constructing high-accuracy models. Therefore, in this study, a traffic information prediction model is developed based on highway vehicle detection system (VDS) data and LSTM, and an impact assessment is conducted through changes in the LSTM evaluation indicators for each hyperparameter. In addition, a methodology for finding hyperparameters suitable for predicting highway traffic information in the transportation field is presented.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.18 no.4
• /
• pp.81-99
• /
• 2015

The acceleration of global warming has required better understanding of carbon cycles over local and regional areas such as the Korean peninsula. Since forests serve as a carbon sink, which stores a large amount of terrestrial carbon, there has been a demand to accurately estimate such forest carbon sequestration. In Korea, the National Forest Inventory(NFI) has been used to estimate the forest carbon stocks based on the amount of growing stocks per hectare measured at sampled location. However, as such data are based on point(i.e., plot) measurements, it is difficult to identify spatial distribution of forest carbon stocks. This study focuses on urban areas, which have limited number of NFI samples and have shown rapid land cover change, to estimate grid-based forest carbon stocks based on UNFCCC Approach 3 and Tier 3. Land cover change and forest carbon stocks were estimated using Landsat 5 TM data acquired in 1991, 1992, 2010, and 2011, high resolution airborne images, and the 3rd, 5th~6th NFI data. Machine learning techniques(i.e., random forest and support vector machines/regression) were used for land cover change classification and forest carbon stock estimation. Forest carbon stocks were estimated using reflectance, band ratios, vegetation indices, and topographical indices. Results showed that 33.23tonC/ha of carbon was sequestrated on the unchanged forest areas between 1991 and 2010, while 36.83 tonC/ha of carbon was sequestrated on the areas changed from other land-use types to forests. A total of 7.35 tonC/ha of carbon was released on the areas changed from forests to other land-use types. This study was a good chance to understand the quantitative forest carbon stock change according to the land cover change. Moreover the result of this study can contribute to the effective forest management.

• Journal of the Korean earth science society
• /
• v.29 no.2
• /
• pp.175-188
• /
• 2008

The purposes of this study were to develop an Earth systems-based earth science module and to investigate the effects of field application. The module was applied to two classrooms of a total of 76 second-year high schoolers, in order to investigate the effectiveness of the developed module. Data was collected from observations in earth science classrooms, interviews, and questionnaires. The findings were as follows. First, the Earth systems-based earth science module was designed to be associated with the aims of the national Earth Science Curriculum and to improve students' Earth science literacy. The module was composed of two sections for a total of seven instructional hours for high schoolers. The former sections included the understanding of the Earth system through the understanding of each individual component of the system, its characteristics, properties and structure. The latter section of the module, consisting of 4 instructional hours, dealt with earth environmental problems, the understanding of subsystems changing through natural processes and cycles, and human interactions and their effects upon Earth systems. Second, the module was helpful in learning about the importance of understanding the interactions between water, rock, air, and life when it comes to understanding the Earth system, its components, characteristics, and properties. The Earth systems-based earth science module is a valuable and helpful instructional material which can enhance students' understanding of Earth systems and earth science literacy.

• Journal of The Korean Association For Science Education
• /
• v.37 no.1
• /
• pp.9-16
• /
• 2017

The result of TIMSS 2015 was announced at the end of 2016. In this research, we conducted test-curriculum matching analysis for 8th grade earth science and analyzed Korean students' percentage of correct answers and responses for TIMSS earth science test items. According to the results, Korean students showed high percentage of correct answers when the item topics are covered in the 2009 revised science curriculum, and Korean students revealed their weakness in constructed response items since the percentage for correct answers on constructed response items is half that of multiple choice items. Depending on the earth science topic, for 'solid earth' area, which includes earth's structure and physical features, as well as earth's processes and history, students showed high percentage of correct answers for multiple choice items. Students, however, showed low percentage of correct answers for items that require applying knowledge to everyday situations and connecting with other areas of science such as biology. For 'atmosphere and ocean' areas, which include earth's processes and cycles, students showed low percentage of scores for climate comparison between regions, features of global warming, etc. For the area of 'universe', students showed high percentage of scores for the earth's rotation and revolution, the moon's gravity, and so on because they have learned these topics since primary school. Discussed in the conclusion are ways to secure content connection between the primary and middle school earth science curriculums, ways to develop students' science-inquiry related competencies, and so on to improve middle school earth science curriculum as well as teaching and learning.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.12 no.4
• /
• pp.241-263
• /
• 2010

KoFlux is a Korean network of micrometeorological tower sites that use eddy covariance methods to monitor the cycles of energy, water, and carbon dioxide between the atmosphere and the key terrestrial ecosystems in Korea. KoFlux embraces the mission of AsiaFlux, i.e. to bring Asia's key ecosystems under observation to ensure quality and sustainability of life on earth. The main purposes of KoFlux are to provide (1) an infrastructure to monitor, compile, archive and distribute data for the science community and (2) a forum and short courses for the application and distribution of knowledge and data between scientists including practitioners. The KoFlux community pursues the vision of AsiaFlux, i.e., "thinking community, learning frontiers" by creating information and knowledge of ecosystem science on carbon, water and energy exchanges in key terrestrial ecosystems in Asia, by promoting multidisciplinary cooperations and integration of scientific researches and practices, and by providing the local communities with sustainable ecosystem services. Currently, KoFlux has seven sites in key terrestrial ecosystems (i.e., five sites in Korea and two sites in the Arctic and Antarctic). KoFlux has systemized a standardized data processing based on scrutiny of the data observed from these ecosystems and synthesized the processed data for constructing database for further uses with open access. Through publications, workshops, and training courses on a regular basis, KoFlux has provided an agora for building networks, exchanging information among flux measurement and modelling experts, and educating scientists in flux measurement and data analysis. Despite such persistent initiatives, the collaborative networking is still limited within the KoFlux community. In order to break the walls between different disciplines and boost up partnership and ownership of the network, KoFlux will be housed in the National Center for Agro-Meteorology (NCAM) at Seoul National University in 2011 and provide several core services of NCAM. Such concerted efforts will facilitate the augmentation of the current monitoring network, the education of the next-generation scientists, and the provision of sustainable ecosystem services to our society.