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.     

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.     

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.     

Integrated simulation framework for the process planning of ships and offshore structures

Recently, requests for accurate process planning using simulation have been increasing in many engineering fields, including the shipbuilding industry. To date, designers of shipyards have developed in-house simulation systems or used commercial systems such as the QUEST by Dassault system when requests for the simulation of process planning have occurred. However, these methods have some limitations. First, it requires a lot of time to develop a new in-house simulation system. In addition, it is hard to reuse previously developed systems when developing a new one and it is also hard for these to satisfy the various needs of shipyards effectively.     

A fuzzy ensemble of parallel polynomial neural networks with information granules formed by fuzzy clustering

In this paper, we introduce a new category of fuzzy models called a fuzzy ensemble of parallel polynomial neural network (FEP²N²), which consist of a series of polynomial neural networks weighted by activation levels of information granules formed with the use of fuzzy clustering. The two underlying design mechanisms of the proposed networks rely on information granules resulting from the use of fuzzy C-means clustering (FCM) and take advantage of polynomial neural networks (PNNs).The resulting model comes in the form of parallel polynomial neural networks. In the design procedure, in order to estimate the optimal values of the coefficients of polynomial neural networks we use a weighted least square estimation algorithm. We incorporate various types of structures as the consequent part of the fuzzy model when using the learning algorithm. Among the diverse structures being available, we consider polynomial neural networks, which exhibit highly nonlinear characteristics when being viewed as local learning models.We use FCM to form information granules and to overcome the high dimensionality problem. We adopt PNNs to find the optimal local models, which can describe the relationship between the input variables and output variable within some local region of the input space.We show that the generalization capabilities as well as the approximation abilities of the proposed model are improved as a result of using polynomial neural networks. The performance of the network is quantified through experimentation in which we use a number of benchmarks already exploited within the realm of fuzzy or neurofuzzy modeling.