Yeungnam University

About: Yeungnam University is a education organization based out in Daegu, South Korea. It is known for research contribution in the topics: Thin film & Catalysis. The organization has 9885 authors who have published 22075 publications receiving 372798 citations.

Regulation of notch1 signaling by nrf2: implications for tissue regeneration

Abstract: The Keap1-Nrf2-ARE signaling pathway elicits an adaptive response for cell survival after endogenous and exogenous stresses, such as inflammation and carcinogens, respectively. Keap1 inhibits the transcriptional activation activity of Nrf2 (p45 nuclear factor erythroid-derived 2-related factor 2) in unstressed cells by facilitating its degradation. Through transcriptional analyses in Keap1- or Nrf2-disrupted mice, we identified interactions between the Keap1-Nrf2-ARE and the Notch1 signaling pathways. We found that Nrf2 recognized a functional antioxidant response element (ARE) in the promoter of Notch1. Notch1 regulates processes such as proliferation and cell fate decisions. We report a functional role for this cross talk between the two pathways and show that disruption of Nrf2 impeded liver regeneration after partial hepatectomy and was rescued by reestablishment of Notch1 signaling.

Application of the recurrent multilayer perceptron in modeling complex process dynamics

Abstract: A nonlinear dynamic model is developed for a process system, namely a heat exchanger, using the recurrent multilayer perceptron network as the underlying model structure. The perceptron is a dynamic neural network, which appears effective in the input-output modeling of complex process systems. Dynamic gradient descent learning is used to train the recurrent multilayer perceptron, resulting in an order of magnitude improvement in convergence speed over a static learning algorithm used to train the same network. In developing the empirical process model the effects of actuator, process, and sensor noise on the training and testing sets are investigated. Learning and prediction both appear very effective, despite the presence of training and testing set noise, respectively. The recurrent multilayer perceptron appears to learn the deterministic part of a stochastic training set, and it predicts approximately a moving average response of various testing sets. Extensive model validation studies with signals that are encountered in the operation of the process system modeled, that is steps and ramps, indicate that the empirical model can substantially generalize operational transients, including accurate prediction of instabilities not in the training set. However, the accuracy of the model beyond these operational transients has not been investigated. Furthermore, online learning is necessary during some transients and for tracking slowly varying process dynamics. Neural networks based empirical models in some cases appear to provide a serious alternative to first principles models. >

Survey of NLOS identification and error mitigation problems in UWB-based positioning algorithms for dense environments

Abstract: In this survey, the currently available ultra-wideband-based non-line-of-sight (NLOS) identification and error mitigation methods are presented. They are classified into several categories and their comparison is presented in two tables: one each for NLOS identification and error mitigation. NLOS identification methods are classified based on range estimates, channel statistics, and the actual maps of the building and environment. NLOS error mitigation methods are categorized based on direct path and statistics-based detection.

Antimicrobial, Antioxidant and Cytotoxic Activity of Silver Nanoparticles Synthesized by Leaf Extract of Erythrina suberosa (Roxb.).

Abstract: In this experiment, biosynthesized silver nanoparticles were synthesized using aqueous leaf extract of Erythrina suberosa (Roxb.).The biosynthesis of silver nanoparticle was monitored using ultraviolet-visible spectroscopy. The effect of the phytoconstituents present in E. suberusa, on formation of stable silver nanoparticles was analyzed by fourier-transform infrared spectroscopy. The size, charge and polydispersity nature of silver nanoparticles were studied using dynamic light scattering spectroscopy. The morphology of the nanoparticles was determined by scanning electron microscopy. The result indicates that the glycosides, flavonoids and phenolic compounds present in the plant extract played a major role in the biosynthesis of silver nanoparticles. The antimicrobial activities of nanoparticles were evaluated against different pathogenic bacterium and fungi. The antioxidant property was studied by radical scavenging (DPPH) assay and cytotoxic activity was evaluated against A-431 osteosarcoma cell line by MTT assay. The characteristics of the synthesized silver nanoparticles suggest their application as a potential antimicrobial and anticancer agent.

IMC−PID Controller Design for Improved Disturbance Rejection of Time-Delayed Processes

Abstract: The IMC-PID tuning rules demonstrate good set-point tracking but sluggish disturbance rejection, which becomes severe when a process has a small time-delay/time-constant ratio. In this study, an optimal internal model control (IMC) filter structure is proposed for several representative process models to design a proportional-integral-derivative (PID) controller that produces an improved disturbance rejection response. The simulation studies of several process models show that the proposed design method provides better disturbance rejection for lag-time dominant processes, when the various controllers are all tuned to have the same degree of robustness according to the measure of maximum sensitivity. The robustness analysis is conducted by inserting a perturbation in each of the process parameters simultaneously, with the results demonstrating the robustness of the proposed controller design with parameter uncertainty. A closed-loop ‚