• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.3
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• pp.66-81
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• 2021

In this paper, we review the lowest order staggered discontinuous Galerkin methods on polygonal meshes in 2D. The proposed method offers many desirable features including easy implementation, geometrical flexibility, robustness with respect to mesh distortion and low degrees of freedom. Discrete function spaces for locally H¹ and H(div) spaces are considered. We introduce special properties of a sub-mesh from a given star-shaped polygonal mesh which can be utilized in the construction of discrete spaces and implementation of the staggered discontinuous Galerkin method. For demonstration purposes, we consider the lowest case for the Poisson equation. We emphasize its efficient computational implementation using only geometrical properties of the underlying mesh.

• Journal of Ecology and Environment
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• v.45 no.1
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• pp.40-53
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• 2021

The level of soil organic carbon (SOC) fluctuates in different types of forest stands: this variation can be attributed to differences in tree species, and the variables associated with soil, climate, and topographical features. The present review evaluates the level of SOC in different types of forest stands to determine the factors responsible for the observed variation. Mixed stands have the highest amount of SOC, while coniferous (both deciduous-coniferous and evergreen-coniferous) stands have greater SOC concentrations than deciduous (broadleaved) and evergreen (broadleaved) tree stands. There was a significant negative correlation between SOC and mean annual temperature (MAT) and sand composition, in all types of forest stands. In contrast, the silt fraction has a positive correlation with SOC, in all types of tree stands. Variation in SOC under different types of forest stands in different landscapes can be due to differences in MAT, and the sand and silt fraction of soil apart from the type of forests.

• Journal of Plant Biotechnology
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• v.49 no.1
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• pp.3-14
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• 2022

Leonotis nepetifolia (L.) R. Br, commonly called dagga, klip dagga, or lion's ear, has been used to effectively treat various diseases and other health problems for a long time because of its antimicrobial, anti-inflammatory, antioxidant, and analgesic activities. Several studies have attributed these biological activities to L. nepetifolia's constituent secondary metabolites, such as alkaloids, phenolics, flavonoids, tannins, steroids, glycosides, coumarins, anthocyanins, and saponins. This review aims to examine the evidence-based ethnopharmacological uses of L. nepetifolia in the treatment of bronchial asthma, diarrhea, skin diseases, malaria, burns, cancer, diabetes mellitus, and rheumatism. However, although L. nepetifolia has great potential to treat these diseases, further isolation and identification of its therapeutic phytochemical constituents are required. In addition, the performance of its extracts and phytochemicals should be thoroughly tested in preclinical and clinical trials in order to ascertain their safety and efficacy, which will prove valuable in developing new medicines.

• The Korean Journal of Physiology and Pharmacology
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• v.26 no.5
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• pp.307-312
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• 2022

It is well known that dopamine transmission from the ventral tegmental area (VTA) modulates motivated behavior and reinforcement learning. Although dopaminergic neurons are the major type of VTA neurons, recent studies show that a significant proportion of the VTA contains GABAergic and type 2 vesicular glutamate transporter (VGLUT2)-positive neurons. The non-dopaminergic neurons are also critically involved in regulating motivated behaviors. Some VTA neurons appear to co-release two different types of neurotransmitters. They are VGLUT2-DA neurons, VGLUT2-GABA neurons and GABA-DA neurons. These co-releasing neurons show distinct features compared to the neurons that release a single neurotransmitter. Here, we review how VTA cell populations wire to the other brain regions and how these projections differentially contribute to motivated behavior through the distinct molecular mechanism. We summarize the activities, projections and functions of VTA neurons concerning motivated behavior. This review article discriminates VTA cell populations related to the motivated behavior based on the neurotransmitters they release and extends the classical view of the dopamine-mediated reward system.

• BMB Reports
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• v.56 no.8
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• pp.426-438
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• 2023

Muscle wasting in end-stage renal disease (ESRD) is an escalating issue due to the increasing global prevalence of ESRD and its significant clinical impact, including a close association with elevated mortality risk. The phenomenon of muscle wasting in ESRD, which exceeds the rate of muscle loss observed in the normal aging process, arises from multifactorial processes. This review paper aims to provide a comprehensive understanding of muscle wasting in ESRD, covering its epidemiology, underlying molecular mechanisms, and current and emerging therapeutic interventions. It delves into the assessment techniques for muscle mass and function, before exploring the intricate metabolic and molecular pathways that lead to muscle atrophy in ESRD patients. We further discuss various strategies to mitigate muscle wasting, including nutritional, pharmacological, exercise, and physical modalities intervention. This review seeks to provide a solid foundation for future research in this area, fostering a deeper understanding of muscle wasting in ESRD, and paving the way for the development of novel strategies to improve patient outcomes.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.393-400
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• 2022

Agent-based modeling (ABM), as a relatively new simulation technique, has recently gained in popularity in the civil engineering domain due to its uniquely advantageous features. Among many civil engineering applications, ABM has been applied to facility operation and management, such as energy consumption management, as well as the enhancement of maintenance and repair processes. The former studies used ABM to manage energy consumption through simulating human energy-related behaviors and their interactions with facilities, as well as electrical, heating, and cooling systems and appliances, while the latter used ABM to enhance maintenance process through facilitating coordination, negotiation, and decision making between facility managers, service providers, and repair workers. The present study aims to provide a short qualitative review on the most recent applications of ABM in the above-mentioned areas. Based on the review and follow-up analysis, the study identifies the advantages, disadvantages, and limitations of ABM applications to facility operation and management, and offers several potential future research topics in the hope of filling the existing literature gaps.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.161-176
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• 2022

There has been active research in image classification using deep learning convolutional neural network (CNN) models. ImageNet large-scale visual recognition challenge (ILSVRC) (2010-2017) was one of the most important competitions that boosted the development of efficient deep learning algorithms. This paper introduces and compares six monumental models that achieved high prediction accuracy in ILSVRC. First, we provide a review of the models to illustrate their unique structure and characteristics of the models. We then compare those models under a unified framework. For this reason, additional devices that are not crucial to the structure are excluded. Four popular data sets with different characteristics are then considered to measure the prediction accuracy. By investigating the characteristics of the data sets and the models being compared, we provide some insight into the architectural features of the models.

• Korea Science and Art Forum
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• v.30
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• pp.217-230
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• 2017

The purpose of this study is to develop a theoretical model identifying how consumers accept a fashionable technology product. A systematic review of 39 influential TAM studies focusing on theory development in the context of information technology results in three tendencies, which become backbone of the model of fashionable technology acceptance. A subsequent review of the nature of fashion fleshes out the backbone with detailed propositions in the more specific context of fashionable technology. The model of fashionable technology acceptance includes key propositions of Theory of Reasoned Action, in which internal beliefs consist of functional, aesthetic and symbolic values, and other factors, such as technology features (i.e., hardware specification, software specification, brand, and price factors), environmental conditions (i.e., technical infrastructure and user occasion), individual differences (i.e., age, gender, experience, personality, aesthetic sense, fashion innovativeness, and income), and social influence (i.e., subjective norms, social reputation, and cultural difference). Implications, limitations, and suggestions for future research are also discussed.

• Molecules and Cells
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• v.46 no.2
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• pp.106-119
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• 2023

With the increased number of single-cell RNA sequencing (scRNA-seq) datasets in public repositories, integrative analysis of multiple scRNA-seq datasets has become commonplace. Batch effects among different datasets are inevitable because of differences in cell isolation and handling protocols, library preparation technology, and sequencing platforms. To remove these batch effects for effective integration of multiple scRNA-seq datasets, a number of methodologies have been developed based on diverse concepts and approaches. These methods have proven useful for examining whether cellular features, such as cell subpopulations and marker genes, identified from a certain dataset, are consistently present, or whether their condition-dependent variations, such as increases in cell subpopulations in particular disease-related conditions, are consistently observed in different datasets generated under similar or distinct conditions. In this review, we summarize the concepts and approaches of the integration methods and their pros and cons as has been reported in previous literature.

• Fisheries and Aquatic Sciences
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• v.26 no.7
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• pp.423-436
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• 2023

The objectives of this review were to synthesize the community structure of phytoplankton and the role of atelomix in the phytoplankton dynamics in Ethiopian highland lakes. Changes in a lake's physical structure, light dynamics, and availability of nutrients are closely associated with phytoplankton ecology, and phytoplankton assemblages provide insight into phytoplank- ton responses to these environmental changes. Based on the available information, a total of 173 species of phytoplankton are grouped under seven classes, Chlorophyceae (80 taxa), Bacillariophyceae (55 taxa), Cyanophyceae (24 taxa), Dinophyceae (6 taxa), Eugleonophyceae (6 taxa), Xanthophyceae (1 taxon), and Cryptophyceae (1 taxon) were recorded in five different tropical Ethiopian highland lakes. Chlorophyceae and Bacillariophyceae dominated in terms of species composition. Partial atelomixis, seasonality, and low nutrient concentrations seem to be the main drivers in structuring phytoplankton composition and abun-dances in Ethiopian highland lakes, characterized by a high diversity of atelomix-dependent benthic diatoms and desmids. Thus, this review will help understand the role of atelomix and nutrient availability in the phytoplankton composition and biomass of tropical highland lakes of Ethiopia.