Effect of ionic content,solid content,degree of neutralization,and chain extension on aqueous polyurethane dispersions prepared by prepolymer method

There are many variables in the preparation of aqueous polyurethane (PU) dispersions. Carboxylic acid content, solid content, degree of pre/postneutralization of the carboxylic acids, and chain extension all impact dispersion particle size, viscosity, pH, molecular weights, and glass transition temperature. This study evaluated the impact of these variables on a given PU dispersion formulation prepared from isophorone diisocyanate, an aliphatic polyester polyol, dimethylol propionic acid, and hexamethylene diamine with triethyl amine as the neutralizing base and N‐methyl pyrrolidone as the cosolvent. Changes in carboxylic acid content, degree of preneutralization, and chain extension were found to have the expected impacts on dispersions properties. Increased ionic content in the dispersion step led to lower particle size and higher viscosity, increased chain extension with its concurrent increase in molecular improved subsequent film properties. Surprising results were obtained by varying the amount of postneutralization and from increased solids content at the time of dispersion. Unexpectedly, both of these variations led to much higher dispersion viscosities and particle size in solution. To have these changes take place, it is theorized that there is a major change in solution morphology caused by these modifications. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 98: 2514–2520, 2005     

TP63 Is Significantly Upregulated in Diabetic Kidney

The role of tumor protein 63 (TP63) in regulating insulin receptor substrate 1 (IRS-1) and other downstream signal proteins in diabetes has not been characterized. RNAs extracted from kidneys of diabetic mice (db/db) were sequenced to identify genes that are involved in kidney complications. RNA sequence analysis showed more than 4- to 6-fold increases in TP63 expression in the diabetic mice’s kidneys, compared to wild-type mice at age 10 and 12 months old. In addition, the kidneys from diabetic mice showed significant increases in TP63 mRNA and protein expression compared to WT mice. Mouse proximal tubular cells exposed to high glucose (HG) for 48 h showed significant decreases in IRS-1 expression and increases in TP63, compared to cells grown in normal glucose (NG). When TP63 was downregulated by siRNA, significant increases in IRS-1 and activation of AMP-activated protein kinase (AMPK (p-AMPK-Th¹⁷²)) occurred under NG and HG conditions. Moreover, activation of AMPK by pretreating the cells with AICAR resulted in significant downregulation of TP63 and increased IRS-1 expression. Ad-cDNA-mediated over-expression of tuberin resulted in significantly decreased TP63 levels and upregulation of IRS-1 expression. Furthermore, TP63 knockdown resulted in increased glucose uptake, whereas IRS-1 knockdown resulted in a decrease in the glucose uptake. Altogether, animal and cell culture data showed a potential role of TP63 as a new candidate gene involved in regulating IRS-1 that may be used as a new therapeutic target to prevent kidney complications in diabetes.     

Broadband dielectric spectroscopy of nanostructured maleated polypropylene/polycarbonate blends prepared by in situ polymerization and compatibilization

The miscibility and molecular dynamics of nanostructured maleated polypropylene (mPP)/polycarbonate (PC) blends prepared by in situ polymerization of macrocyclic carbonates with polypropylene modified with 0.5 wt% of maleic anhydride-reactive groups were investigated over a wide range of frequencies (10⁻²-0.5 × 10⁷ Hz) at different constant temperatures using broadband dielectric spectroscopy and scanning transmission electron microscope (STEM). The molecular dynamics of the glass relaxation process of the blend (α-relaxation process) appeared at a lower temperature range compared with that of the pure PC. This shift in the molecular relaxation process is attributed to the partial miscibility of the two polymer components in the blends as previously confirmed by the morphology via STEM. Nanoscale morphologies with average domain diameters as small as 50 nm were obtained for the different blend compositions studied. The STEM photographs show that the graft mPP-g-PC prefers to locate at the interfaces as previously reported. The relaxation spectrum of pure PC and mPP/PC blends was resolved into α- and β-relaxation processes using the Havriliak-Negami equation and ionic conductivity. The dielectric relaxation parameters, such as relaxation peak broadness, maximum frequency, f_max, and dielectric strength, Δ? (for the α- and β-relaxation processes), were found to be blend composition dependent. The kinetics of the α-relaxation processes of the blends were well described by Vogel-Fulcher-Tammann (VFT) equation. The local process of PC was resolved into two relaxation processes β₁ and β₂, associated with the carbonyl groups' motion and the combined motions of carbonyl and phenylene groups, respectively. Only β₂ shifted to lower frequency in the blend while β₁ was relatively not affected by blending. The electric modulus of the blends was used to get a sufficient resolution of the different relaxation processes in the samples, i.e., α-, β-relaxation processes, ionic conductivity, and interfacial polarization. In addition, the blending method used was found to increase the d.c. conductivity without affecting the charge carrier transport mechanism, making it possible to develop novel polymer blends with tunable dielectric properties and morphology from existing polymers.     

Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.

     

Maximal limited similarity-based rough set model

Non-symmetric similarity relation-based rough set model (NS-RSM) is viewed as mathematical tool to deal with the analysis of imprecise and uncertain information in incomplete information systems with “?” values. NS-RSM relies on the concept of non-symmetric similarity relation to group equivalent objects and generate knowledge granules that are then used to approximate the target set. However, NS-RSM results in unpromising approximation space when addressing inconsistent data sets that have lots of boundary objects. This is because objects in the same similarity classes are not necessarily similar to each other and may belong to different target classes. To enhance NS-RSM capability, we introduce the maximal limited similarity-based rough set model (MLS-RSM) which describes the maximal collection of indistinguishable objects that are limited tolerance to each other in similarity classes. This allows accurate computation to be done for the approximation space. Furthermore, approximation accuracy comparisons have been conducted among NS-RSM and MLS-RSM. The results demonstrate that MLS-RSM model outperforms NS-RSM and can approximate the target set more efficiently.     

Design of Bang-bang Controller Based on a Fuzzy-Neuro Approach: Application to a Heating System

A fuzzy-neuro approach for the design of bang-bang controller is presented in this paper. The approach has been used with success for the time optimal bang-bang control of a heating system. The improved bang-bang controller suppresses the oscillations often observed at the output of an on-off controller. A fuzzy system is used for the implementation of the on-off control. An extension of the fuzzy control is provided by an equivalent neural network of the fuzzy system. A test application, that of a house heating with a two-state furnace, is developed and evaluated with standard hysteresis switching, fuzzy control, and fuzzy-neuro control.     

A model for measuring complexity of automated and hybrid assembly systems

The demand for delivering product variety has been increasing. Increased product variety caused by product customization, personalization, evolution and changes in their manufacturing systems. Variety allows manufacturers to satisfy a wide range of customer requirements, but it can also be a major contributing factor to increased complexity of assembly. Complexity is generally believed to be one of the main causes of the present challenges in manufacturing systems such as lengthy and costly design processes, higher life cycle costs and the existence of numerous failure modes. Complex assembly systems are costly to implement, run, control and maintain. Assessing complexity of assembly helps guides designers in creating assembly-oriented product designs and following steps to reduce and manage sources of assembly complexity. On the other hand, reducing complexity of assembly helps lower assembly cost and time, improves productivity and quality and increases profitability and competitiveness. The complexity of assembly should be assessed by considering both products and their assembly systems. In this paper, a structural classification coding scheme has been used to measure assembly systems complexity. It considers the inherent structural complexity of typical assembly equipment. The derived assembly systems complexity accounts for the number, diversity and information content within each class of the assembly system modules. A domestic appliance drive assembly system is used to demonstrate the use of the classification code to calculate the assembly system complexity. The developed complexity metrics can be used by designers as decision support tools to compare and rationalize various automated assembly systems alternatives and select the design that meets the requirements while reducing potential assembly complexity and associated cost.     

Stochastic optimization of nonlinear energy sinks

Nonlinear energy sinks (NES) are a promising technique to achieve vibration mitigation. Through nonlinear stiffness properties, NES are able to passively and irreversibly absorb energy. Unlike the traditional Tuned Mass Damper (TMD), NES absorb energy from a wide range of frequencies. Many studies have focused on NES behavior and dynamics, but few have addressed the optimal design of NES. Design considerations of NES are of prime importance as it has been shown that NES dynamics exhibit an acute sensitivity to uncertainties. In fact, the sensitivity is so marked that NES efficiency is near-discontinuous and can switch from a high to a low value for a small perturbation in design parameters or loading conditions. This article presents an approach for the probabilistic design of NES which accounts for random design and aleatory variables as well as response discontinuities. In order to maximize the mean efficiency, the algorithm is based on the identification of regions of the design and aleatory space corresponding to markedly different NES efficiencies. This is done through a sequence of approximated sub-problems constructed from clustering, Kriging approximations, a support vector machine, and Monte-Carlo simulations. The refinement of the surrogates is performed locally using a generalized max-min sampling scheme which accounts for the distributions of random variables. The sampling scheme also makes use of the predicted variance of the Kriging surrogates for the selection of aleatory variables values. The proposed algorithm is applied to three example problems of varying dimensionality, all including an aleatory excitation applied to the main system. The stochastic optima are compared to NES optimized deterministically.     

Exact and approximate analytic solutions of the nonlinear convective fin problem with temperature-dependent thermal conductivity

This article shows that the well-known nonlinear boundary value problem of the differential equation for temperature distribution of convective straight fins with temperature-dependent thermal conductivity is exactly solvable in an implicit form. Furthermore, an exact solution in an explicit form is derived. Also, an accurate analytic solution (series solution) is obtained by a new variation of the Adomian decomposition method.