• 대한치주과학회:학술대회논문집
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• 2006.11a
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• pp.116-117
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• 2006

• 대한치주과학회:학술대회논문집
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• 2007.11a
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• pp.131-132
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• 2007

• 대한치주과학회:학술대회논문집
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• 2007.11a
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• pp.45-46
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• 2007

• BMB Reports
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• v.44 no.1
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• pp.22-27
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• 2011

A series of elastin-like proteins, $SKGPG[V(VKG)_3VKVPG]_n$-(ELP1-90)WP (n = 1, 2, 3, and 4), were biosynthesized based on the hydrophobic and lysine linkage domains of tropoelastin. The formation of self-assembled hydrophobic aggregates was monitored in order to determine the influence of cationic segments on phase transition properties as well as the sensitivity to changes in salt and pH. The thermal transition profiles of the proteins fused with only one or two cationic blocks (n = 1 or 2) were similar to that of the counterpart ELP1-90. In contrast, diblock proteins that contain 3 and 4 cationic blocks displayed a triphasic profile and no transition, respectively. Upon increasing the salt concentration and pH, a stimulus-induced phase transition from a soluble conformation to an insoluble aggregate was observed. The effects of cationic segments on the stimuli sensitivity of cationic bimodal ELPs were interpreted in terms of their structural and molecular characteristics.

• Advances in nano research
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• v.17 no.2
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• pp.109-124
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• 2024

In the era of smart manufacturing, precise prediction of springback-a common issue in ultra-thin sheet metal forming- and forming limits are critical for ensuring high-quality production and minimizing waste. This paper presents a novel approach that leverages the Internet of Things (IoT) and Artificial Neural Networks (ANN) to enhance springback and forming limits prediction accuracy. By integrating IoT-enabled sensors and devices, real-time data on material properties, forming conditions, and environmental factors are collected and transmitted to a central processing unit. This data serves as the input for an ANN model, which is trained with crystal plasticity simulations and experimental data to predict springback with high precision. Our proposed system not only provides continuous monitoring and adaptive learning capabilities but also facilitates real-time decision-making in manufacturing processes. Experimental results demonstrate significant improvements in prediction accuracy compared to traditional methods, highlighting the potential of IoT and ANN integration in advancing smart manufacturing. This approach promises to revolutionize quality control and operational efficiency in the industry, paving the way for more intelligent and responsive manufacturing systems.

• Structural Engineering and Mechanics
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• v.84 no.3
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• pp.393-411
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• 2022

A high-performance reactive powder concrete (RPC) has been readied alongside river sand, with 1.25 mm particle size when under the condition of 80C steam curing. As a heat and sound insulation, expanded perlite aggregate (EPA) provides economic advantages in building. Concrete containing EPA is examined in terms of cement types (CEM II 32.5R and CEM I 42.5R), doses (0, 2%, 4% and 6%) as well as replacement rates in this research study. The compressive and density of concrete were used in the testing. At the end of the 28-day period, destructive and nondestructive tests were performed on cube specimens of 150 mm150 mm150 mm. The concrete density is not decreased with the addition of more perlite (from 45 to 60 percent), since the enlarged perlite has a very low barrier to crushing. To get a homogenous and fluid concrete mix, longer mixing times for all the mix components are necessary due to the higher amount of perlite. As a result, it is not suggested to use greater volumes of this aggregate in RPC. In the presence of de-icing salt, the lightweight RPC exhibits excellent freeze-thaw resistance (mass is less than 0.2 kg/m²). The addition of perlite strengthens the aggregate-matrix contact, but there is no apparent ITZ. An increased compressive strength was seen in concretes containing expanded perlite powder and steel fibers with good performance.