• Computational Structural Engineering
• /
• v.9 no.2
• /
• pp.93-101
• /
• 1996

An artificial neural network is a computational model that mimics the biological system of the brain and it consists of a number of interconnected processing units where it can reasonably infer by them. Because the neural network is particularly useful for evaluating systems with a multitude of nonlinear variables, it can be used in experimental results predictions, in structural planning and in optimum design of structures. This paper describes the basic theory related to the neural networks and discusses the applicability of neural networks to predict the ultimate shear capacity of the precast concrete vertical joints by comparing the neural networks with a conventional method such as regression.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.11 no.5
• /
• pp.455-461
• /
• 2006

In this study, we have described a method for the fabrication of a protein chip on silicon substrate using hydrophobic thin film and microfluidic channels, for the simultaneous detection of multiple targets in samples. The use of hydrophobic thin film provides for a physical, chemical, and biological barrier for protein patterning. The microfluidic channels create four protein patterned strips on the silicon surfaces with a high signal-to-noise ratio. The feasibility of the protein chips was determined in order to discriminate between each protein interaction in a mixture sample that included biotin, ovalbumin, hepatitis B antigen, and hepatitis C antigen. In the fabrication of the multiplexed assay system, the utilization of the hydrophobic thin film and the microfluidic networks constitutes a more convenient method for the development of biosensors or biochips. This technique may be applicable to the simultaneous evaluation of multiple protein-protein interactions.

• Polymer(Korea)
• /
• v.39 no.3
• /
• pp.426-432
• /
• 2015

Poly(ether sulfone) (PES) embedded diglycidylether of bisphenol-A (DGEBA) epoxy composites were fabricated for improving its mechanical properties and thermal stability. The mechanical properties such as tensile, flexural and impact strength of the composites changed significantly with the introduction of PES. The value of the fracture toughness of this composite also was increased remarkably about 24%. Thermal stability of PES/epoxy composites also improved 12%, which was calculated with integral procedural decomposition temperature (IPDT). From the differential scanning calorimeter (DSC) result, the curing temperature and curing heat decreased according to the increase of PES contents. These were attributed to the good distribution and the formation of the semi-interpenetrating polymer networks (semi-IPNs) composed of the epoxy network and linear PES.

• Journal of Appropriate Technology
• /
• v.6 no.2
• /
• pp.238-255
• /
• 2020

As COVID-19, which occurred at the end of 2019, has become a global pandemic, it has emerged as an unprecedented event that quickly destroys a nation's medical and healthcare system in both developed and developing countries. In the 21st century, most of the civil society that aimed for hyperconnected society is facing a new crisis that has not been experienced so far. Indeed, lack of personal protective equipment, isolation of clustered communities, disruption of medical systems necessary for diagnosis and treatment, and disruption of educational and economic activities due to social isolation are emerging. Since the COVID-19 has occurred, many of the difficulties that have occurred in the past six months indicate the basic infrastructure a society should have particularly in a pandemic. These include personal protective equipment (PPE), decontamination and quarantine tools essential for effective response, rapid and precise large-scale diagnosis, medical devices required for patient care, and identification and fast and wide on-line networks that can be used in social isolation. In this first part, we would like to introduce some representative examples of 1) personal protective equipment, 2) prevention of personal and community health, 3) social response through big data and networks within the framework of appropriate technology.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.10
• /
• pp.2054-2061
• /
• 2015

Biologically inspired modeling techniques have received considerable attention for their robustness, scalability, and adaptability with simple local interactions and limited information. Among these modeling techniques, Gene Regulatory Networks (GRNs) play a central role in understanding natural evolution and the development of biological organisms from cells. In this paper, we apply GRN principles to the WSN system and propose a new GRN model for decentralized node scheduling design to achieve energy balancing while meeting delay requirements. Through this scheme, each sensor node schedules its state autonomously in response to gene expression and protein concentration, which are controlled by the proposed GRN-inspired node scheduling model. Simulation results indicate that the proposed scheme achieves superior performance with energy balancing as well as desirable delay compared with other well-known schemes.

• BMB Reports
• /
• v.35 no.1
• /
• pp.7-18
• /
• 2002

The major function of the thymus is to eliminate developing thymocytes that are potentially useless or autoreactive, and select only those that bear functional T cell antigen receptors (TCRs) through fastidious screening. It is believed that glucocorticoids (GCs) are at least in part responsible for cell death during death by neglect. In this review, we will mainly cover the topic of the GC-induced apoptosis of developing thymocytes. We will also discuss how thymocytes that are fated to die by GCs can be rescued from GC-induced apoptosis in. response to a variety of signals with antagonizing properties for GC receptor (GR) signaling. Currently, a lot of evidence supports the notion that the decision is made as a result of the integration of the multiple signal transduction networks that are triggered by GR, TCR, and Notch. A few candidate molecules at the converging point of these multiple signaling pathyways will be discussed. We will particularly describe the role of the SRG3 protein as a potent modulator of GC-induced apoptosis in the crosstalk.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2004.11a
• /
• pp.265-271
• /
• 2004

The interaction network of protein -protein plays an important role to understand the various biological functions of cells. Currently, the high -throughput experimental techniques (two -dimensional gel electrophoresis, mass spectroscopy, yeast two -hybrid assay) provide us with the vast amount of data for protein-protein interaction at the proteome scale. In order to recognize the role of each protein in their network, the efficient bioinformatical and computational analysis methods are required. We propose a systematic and mathematical method which can analyze the protein -protein interaction network rigorously and enable us to capture the biological and physical essence of a topological character and stability of protein -protein network, and sensitivity of each protein along the biological pathway of their network. We set up a Laplacian matrix of spectral graph theory based on the protein-protein network of yeast proteome, and perform an eigenvalue analysis and apply a perturbation method on a Laplacian matrix, which result in recognizing the center of protein cluster, the identity of hub proteins around it and their relative sensitivities. Identifying the topology of protein -protein network via a Laplacian matrix, we can recognize the important relation between the biological pathway of yeast proteome and the formalism of master equation. The results of our systematic and mathematical analysis agree well with the experimental findings of yeast proteome. The biological function and meaning of each protein cluster can be explained easily. Our rigorous analysis method is robust for understanding various kinds of networks whether they are biological, social, economical...etc

• Bulletin of the Korean Chemical Society
• /
• v.30 no.9
• /
• pp.1981-1984
• /
• 2009

Subcellular localization of protein kinase often plays an important role in determining its activity and specificity. Protein kinase C (PKC), a family of multi-gene protein kinases has long been known to be translocated to the particular cellular compartments in response to DAG or its analog phorbol esters. We used C-terminal green fluorescent protein (GFP) fusion proteins of PKC isoforms to visualize the subcellular distribution of individual PKC isoforms. Intracellular localization of PKC-GFP proteins was monitored by fluorescence microscopy after transient transfection of PKC-GFP expression vectors in the HeLa cells. In unstimulated HeLa cells, all PKC isoforms were found to be distributed throughout the cytoplasm with a few exceptions. PKC$\theta$ was mostly localized to the Golgi, and PKC$\gamma$, PKC$\delta$ and PKC$\eta$ showed cytoplasmic distribution with Golgi localization. DAG analog TPA induced translocation of PKC-GFP to the plasma membrane. PKC$\alpha$, PKC$\eta$ and PKC$\theta$ were also localized to the Golgi in response to TPA. Only PKC$\delta$ was found to be associated with the nuclear membrane after transient TPA treatment. These results suggest that specific PKC isoforms are translocated to different intracellular sites and exhibit distinct biological effects.

• Steel and Composite Structures
• /
• v.14 no.4
• /
• pp.367-377
• /
• 2013

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

• Environmental Engineering Research
• /
• v.25 no.4
• /
• pp.515-521
• /
• 2020

Nowadays, due to various pollution sources, it is essential for environmental scientists to monitor water quality. Phytoplanktons form the end of the food chain in water bodies and are one of the most important biological indicators in water pollution studies. Chlorophyll-A, a green pigment, is found in all phytoplankton. Chlorophyll-A concentration indicates phytoplankton biomass directly. Therefore, Chlorophyll-A is an indirect indicator of pollutants, including phosphorus and nitrogen, and their refinement and control are important. The present study, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were used to estimate the chlorophyll-A concentration in southern coastal waters in the Caspian Sea. For this purpose, Multi-layer perceptron neural networks (NNs) were applied which contained three and four feed-forward layers. The best three-layer NN has 15 neurons in its hidden layer and the best four-layer one has 5 in each. The three- and four- layer networks both resulted in similar root mean square errors (RMSE), 0.1($\frac{{\mu}g}{l}$), however, the four-layer NNs proved superior in terms of R² and also required less training data. Accordingly, a four-layer feed-forward NN with 5 neurons in each hidden layer, is the best network structure for estimating Chlorophyll-A concentration in the southern coastal waters of the Caspian Sea.