• Archives of Pharmacal Research
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• v.25 no.5
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• pp.572-584
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• 2002

Rapid development in molecular biology and recent advancement in recombinant technology increase identification and commercialization of potential protein drugs. Traditional forms of administrations for the peptide and protein drugs often rely on their parenteral injection, since the bioavailability of these therapeutic agents is poor when administered nonparenterally. Tremendous efforts by numerous investigators in the world have been put to improve protein formulations and as a result, a few successful formulations have been developed including sustained-release human growth hormone. For a promising protein delivery technology, efficacy and safety are the first requirement to meet. However, these systems still require periodic injection and increase the incidence of patient compliance. The development of an oral dosage form that improves the absorption of peptide and especially protein drugs is the most desirable formulation but one of the greatest challenges in the pharmaceutical field. The major barriers to developing oral formulations for peptides and proteins are metabolic enzymes and impermeable mucosal tissues in the intestine. Furthermore, chemical and conformational instability of protein drugs is not a small issue in protein pharmaceuticals. Conventional pharmaceutical approaches to address these barriers, which have been successful with traditional organic drug molecules, have not been effective for peptide and protein formulations. It is likely that effective oral formulations for peptides and proteins will remain highly compound specific. A number of innovative oral drug delivery approaches have been recently developed, including the drug entrapment within small vesicles or their passage through the intestinal paracellular pathway. This review provides a summary of the novel approaches currently in progress in the protein oral delivery followed by factors affecting protein oral absorption.

• Korean Chemical Engineering Research
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• v.46 no.5
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• pp.949-957
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• 2008

Estimating the first order plus time delay model on the basis of the step responses has been widely used in industry for the tuning of PID controllers. Even though various model identification methods from simple graphical approaches to complicated approaches based on least squares method have been proposed, simple approaches to incorporate noisy step responses are rarely available. In this research, we will compare and analyze recent approaches using the integrals of the step responses and develop an improved identification method to incorporate real situations more effectively.

• Steel and Composite Structures
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• v.18 no.5
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• pp.1259-1277
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• 2015

The present study is focused on the behavior and design of perforated steel storage rack columns under axial compression. These columns may exhibit different types of behavior and levels of strength owing to their peculiar features including their complex cross-section forms and perforations along the member. In the present codes of practice, the design of these columns is carried out using analytical formulas which are supported by experimental tests described in the relevant code document. Recently proposed analytical approaches are used to estimate the load carrying capacity of axially compressed steel storage rack columns. Experimental and numerical studies were carried out to verify the proposed approaches. The experimental study includes compression tests done on members of different lengths, but of the same cross-section. A comparison between the analytical and the experimental results is presented to identify the accuracy of the recently proposed analytical approaches. The proposed approach includes modifications in the Direct Strength Method to include the effects of perforations (the so-called reduced thickness approach). CUFSM and CUTWP software programs are used to calculate the elastic buckling parameters of the studied members. Results from experimental and analytical studies compared very well. This indicates the validity of the recently proposed approaches for predicting the ultimate strength of steel storage rack columns.

• Journal of Agricultural Extension & Community Development
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• v.7 no.2
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• pp.359-376
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• 2000

The purposes of this exploratory study were to interrelate the scholastic discipline of the ‘development communication’ into ‘community development’ and ‘adult education’ in terms of interests, views and theoretical backgrounds of these fields of social sciences; and to draw some implications for developing scholastic interactions among these fields to pursue common social changes of human society. Development communications provide opportunities to set goals, to decide contents, and to utilize communication media in developmental efforts. Contemporary trends of development communication, community development and adult education are concerned with indigenous, two-way, bottom-up and people-centered communication from exogenous, one-way, top-down and institution-centered communication of the past. Multidisciplinary approaches to communication concepts and methodology may increase the potentials of community development and adult education in terms of efficiency and effectiveness. Some of the development communication approaches such as traditional and folk media approach. new media or ICT(information & communication technology) approach, participatory communication for development approach, communication support development approach and mass media approach may be applicable for community development and adult education. Better understanding on development communication approaches will be needed for the adult educators as well as community development practitioners.

• Korean Journal of Child Studies
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• v.25 no.3
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• pp.101-113
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• 2004

The purpose of this study was to compare young children's daily activities in the preschools of two different child-care approaches, i.e., project and traditional approaches. From two preschools. 20 children (M=71.3 months) were observed for 3 consecutive hours. The observers followed the target child, gathering data during 30-second "windows". The window was open every 2 and half minutes. To test the differences between two preschools, phi coefficient tests were used. Results showed that children of traditional approach were more exposed to and engaged in academic activities than those of project approach. In specific, children of traditional approach were more involved in academic and skill/nature lessons. A reverse tendency, however, was found in play activities. Children of project approach were more exposed to and engaged in play activities, especially in play with academic objects. And they were more exposed to conversation with their teacher than their counterparts. Children of project approach also showed more initiatives in their play activities.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.926-940
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• 2018

In clustering-based approaches, cluster heads closer to the sink are usually burdened with much more relay traffic and thus, tend to die early. To address this problem, distance-aware clustering approaches, such as energy-efficient unequal clustering (EEUC), that adjust the cluster size according to the distance between the sink and each cluster head have been proposed. However, the network lifetime of such approaches is highly dependent on the distribution of the sensor nodes, because, in randomly distributed sensor networks, the approaches do not guarantee that the cluster energy consumption will be proportional to the cluster size. To address this problem, we propose a novel approach called CACD (Clustering Algorithm Considering node Distribution), which is not only distance-aware but also node density-aware approach. In CACD, clusters are allowed to have limited member nodes, which are determined by the distance between the sink and the cluster head. Simulation results show that CACD is 20%-50% more energy-efficient than previous work under various operational conditions considering the network lifetime.

• School Mathematics
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• v.5 no.3
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• pp.343-360
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• 2003

In this paper, we deal with the teaching of algebra based on patterns and generalization. The past algebra curriculum starts with letters(variables), algebraic expressions, and equations, but these formal approaching method has many difficulties in the school algebra. Therefore we insist the new algebraic approaches should be needed. In order to develop these instructions, we firstly investigate the relationship of patterns and algebra, the relationship of generalization and algebra, the steps of generalization from patterns and levels of difficulties. Next we look into the algebra instructions based arithmetic patterns, visual patterns and functional situations. We expect that these approaches help students learn algebra when they begin school algebra.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Journal of KIBIM
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• v.9 no.1
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• pp.42-51
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• 2019

Maintenance of aging structures has attracted societal attention. Maintenance of the aging structure can be efficiently performed with a digital twin. In order to maintain the structure based on the digital twin, it is required to accurately detect the damage of the structure. Meanwhile, deep learning-based damage detection approaches have shown good performance for detecting damage of structures. However, in order to develop such deep learning-based damage detection approaches, it is necessary to use a large number of data before and after damage, but there is a problem that the amount of data before and after the damage is unbalanced in reality. In order to solve this problem, this study proposed a method based on Generative adversarial network, one of Generative Model, for generating acceleration data usually used for damage detection approaches. As results, it is confirmed that the acceleration data generated by the GAN has a very similar pattern to the acceleration generated by the simulation with structural analysis software. These results show that not only the pattern of the macroscopic data but also the frequency domain of the acceleration data can be reproduced. Therefore, these findings show that the GAN model can analyze complex acceleration data on its own, and it is thought that this data can help training of the deep learning-based damage detection approaches.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.59-73
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• 2021

There frequently exists inadequacy regarding the number of boreholes installed along tunnel alignment. While geophysical imaging techniques are available for pre-tunnelling geological characterization, they aim to detect specific object (e.g., water body and karst cave). There remains great motivation for the industry to develop a real-time identification technology relating complex geological conditions with the existing tunnelling parameters. This study explores the potential for the use of machine learning-based data driven approaches to identify the change in geology during tunnel excavation. Further, the feasibility for machine learning-based anomaly detection approaches to detect the development of clayey clogging is also assessed. The results of an application of the machine learning-based approaches to Xi'an Metro line 4 are presented in this paper where two tunnels buried in the water-rich sandy soils at depths of 12-14 m are excavated using a 6.288 m diameter EPB shield machine. A reasonable agreement with the measurements verifies their applicability towards widening the application horizon of machine learning-based approaches.