AQP3 Increases Intercellular Cohesion in NSCLC A549 Cell Spheroids through Exploratory Cell Protrusions

Tumor cell aggregation is critical for cell survival following the loss of extracellular matrix attachment and dissemination. However, the underlying mechanotransduction of clustering solitary tumor cells is poorly understood, especially in non-small cell lung cancers (NSCLC). Here, we examined whether cell surface protrusions played an important role in facilitating the physical contact between floating cells detached from a substrate. We employed poly-2-hydroxyethyl methacrylate-based 3D culture methods to mimic in vivo tumor cell cluster formation. The suprastructural analysis of human NSCLC A549 cell spheroids showed that finger-like protrusions clung together via the actin cytoskeleton. Time-lapse holotomography demonstrated that the finger-like protrusions of free-floating cells in 3D culture displayed exploratory coalescence. Global gene expression analysis demonstrated that the genes in the organic hydroxyl transport were particularly enriched in the A549 cell spheroids. Particularly, the knockdown of the water channel aquaporin 3 gene (AQP3) impaired multicellular aggregate formation in 3D culture through the rearrangement of the actomyosin cytoskeleton. Moreover, the cells with reduced levels of AQP3 decreased their transmigration. Overall, these data indicate that cell detachment-upregulated AQP3 contributes to cell surface protrusions through actomyosin cytoskeleton remodeling, causing the aggressive aggregation of free-floating cells dependent on the property of the substratum and collective metastasis.     

Development of geometrically exact new shell elements based on general curvilinear co‐ordinates

In the present study first‐order shear deformable shell finite elements based on general curvilinear co‐ordinates are proposed. For the development of the present shell elements, a partial mixed variational functional with independently assumed strains is provided in order to avoid the severe locking troubles known as transverse shear and membrane lockings. Bubble functions are included in the shape function of displacement to improve the performance of the developed element. The proposed assumed strain four‐ and nine‐node elements based on the general tensor shell theory provide an efficient linkage framework for shell surface modelling and finite element analysis. In the several benchmark problems, the present shell elements with exact geometric representations demonstrate their performance compared to previously reported results. Copyright © 2002 John Wiley & Sons, Ltd.     

Enhanced thermal stability of a sputtered titanium-nitride film as a diffusion barrier for capacitor-bottom electrodes

The effect of a thin RuO_x layer formed on the Ru/TiN/doped poly-Si/Si stack structure was compared with that on the RuO_x/TiN/doped poly-Si/Si stack structure over the post-deposition annealing temperature ranges of 450–600°C. The Ru/TiN/poly-Si/Si contact system exhibited linear behavior at forward bias with a small increase in the total resistance up to 600°C. The RuO_x/TiN/poly-Si/Si contact system exhibited nonlinear characteristics under forward bias at 450°C, which is attributed to no formation of a thin RuO_x layer at the RuO_x surface and porous-amorphous microstructure. In the former case, the addition of oxygen at the surface layer of the Ru film by pre-annealing leads to the formation of a thin RuO_x layer and chemically strong Ru-O bonds. This results from the retardation of oxygen diffusion caused by the discontinuity of diffusion paths. In particular, the RuO_x layer in a nonstoichiometric state is changed to the RuO₂-crystalline phase in a stoichiometric state after post-deposition annealing; this phase can act as an oxygen-capture layer. Therefore, it appears that the electrical properties of the Ru/TiN/poly-Si/Si contact system are better than those of the RuO_x/TiN/poly-Si/Si contact system.     

High-performance error amplifier for fast transient DC-DC converters

A new error amplifier is presented for fast transient response of dc-dc converters. The amplifier has low quiescent current to achieve high power conversion efficiency, but it can supply sufficient current during large-signal operation. Two comparators detect large-signal variations, and turn on extra current supplier if necessary. The amount of extra current is well controlled, so that the system stability can be guaranteed in various operating conditions. The simulation results show that the new error amplifier achieves significant improvement in transient response than the conventional one.     

Observer-based wheelbase preview control of active vehicle suspensions

Optimal observer-based wheelbase preview regulator problem is investigated for active vehicle suspension systems. It is shown that the problem reduces to the classical linear quadratic Gaussian problem, whose solution is well defined, by augmenting dynamics of system and road inputs. The resulting optimal controller is in the form of augmented state feedback controller and this augmented state is estimated by Kalman-Bucy filter using dynamics of the augmented system. Numerical examples of a half car model are given to verify the performance improvement achievable with the proposed controller.     

Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons

In this paper, we propose and investigate a new category of neurofuzzy networks—fuzzy polynomial neural networks (FPNN) endowed with fuzzy set-based polynomial neurons (FSPNs) We develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms (GAs) in particular. The conventional FPNNs developed so far are based on the mechanisms of self-organization, fuzzy neurocomputing, and evolutionary optimization. The design of the network exploits the FSPNs as well as the extended group method of data handling (GMDH). Let us stress that in the previous development strategies some essential parameters of the networks (such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables) being available within the network are provided by the designer in advance and kept fixed throughout the overall development process. This restriction may hamper a possibility of developing an optimal architecture of the model. The design proposed in this study addresses this issue. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of the FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation in which we use a number of modeling benchmarks—synthetic and experimental data being commonly used in fuzzy or neurofuzzy modeling. The obtained results demonstrate the superiority of the proposed networks over the models existing in the references.     

Converting random bits into random numbers

Converting random bits into random numbers is necessary for cryptographic protocols such as key agreements, public key encryptions, digital signatures and so on. In this paper, we propose the simple partial discard method and the complex partial discard method that convert random bits into random numbers. They are up to two times more efficient than standardized techniques.     

Solid circulation characteristics in an internally circulating fluidized bed with orifice-type draft tube

Effects of superficial gas velocities to a draft tube, to an annulus section and particle size on the solid circulation rate (G,) have been determined in an internally circulating fluidized bed (0.28 m I.D. × 2m high) with an orifice type draft tube. The solid circulation rate from the draft tube to an annulus section increases with increasing gas velocities to the draft tube(U _d ) and annulus section (U_a) and consequent increase in pressure drop across the orifice (ΔP_or). However, the values ofG _s decrease by 7–21% with increasing particle size from 86 to 288 μm. The pressure drop across the orifice increases with increasingU _d andU _a . However, ΔP_or decreases by 5–23% with increasing particle size. To predictG _s in an internally circulating fluidized bed, a correlation is proposed as a function of ΔP_or This paper is dedicated to Professor Dong Sup Doh on the occasion of his retirement Korea University.     

Hybrid fuzzy set-based polynomial neural networks and their development with the aid of genetic optimization and information granulation

We introduce a new architecture of feed-forward neural networks called hybrid fuzzy set-based polynomial neural networks (HFSPNNs) that are composed of heterogeneous feed-forward neural networks such as polynomial neural networks (PNNs) and fuzzy set-based polynomial neural networks (FSPNNs). We develop their comprehensive design methodology by embracing mechanisms of genetic optimization and information granulation. The construction of information granulation-driven HFSPNN exploits fundamental technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the resulting information granulation-driven genetically optimized HFSPNN results from a synergistic usage of the hybrid system generated by combining original fuzzy set-based polynomial neurons (FSPNs)-based FSPNN with polynomial neurons (PNs)-based PNN. The design of the conventional genetically optimized HFPNN exploits the extended Group Method of Data Handling (GMDH) whose some essential parameters of the network being tuned with the use of genetic algorithms throughout the overall development process. Two general optimization mechanisms are explored. First, the structural optimization is realized via GAs while the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through extensive experimentation where we considered a number of modeling benchmarks (synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling).     

Highly Exposed ?NH2 Edge on Fragmented g-C3N4 Framework with Integrated Molybdenum Atoms for Catalytic CO2 Cycloaddition: DFT and Techno-Economic Assessment

This study focuses on the applicability of single-atom Mo-doped graphitic carbon nitride (GCN) nanosheets which are specifically engineered with high surface area (exfoliated GCN),  NH₂ rich edges, and maximum utilization of isolated atomic Mo for propylene carbonate (PC) production through CO₂ cycloaddition of propylene oxide (PO). Various operational parameters are optimized, for example, temperature (130 °C), pressure (20 bar), catalyst (Mo₂GCN), and catalyst mass (0.1 g). Under optimal conditions, 2% Mo-doped GCN (Mo₂GCN) has the highest catalytic performance, especially the turnover frequency (TOF) obtained, 36.4 h⁻¹ is higher than most reported studies. DFT simulations prove the catalytic performance of Mo₂GCN significantly decreases the activation energy barrier for PO ring-opening from 50–60 to 4.903 kcal mol⁻¹. Coexistence of Lewis acid/base group improves the CO₂ cycloaddition performance by the formation of coordination bond between electron-deficient Mo atom with O atom of PO, while  NH₂ surface group disrupts the stability of CO₂ bond by donating electrons into its low-level empty orbital. Steady-state process simulation of the industrial-scale consumes 4.4 ton h⁻¹ of CO₂ with PC production of 10.2 ton h⁻¹. Techno-economic assessment profit from Mo₂GCN is estimated to be 60.39 million USD year⁻¹ at a catalyst loss rate of 0.01 wt% h⁻¹.