A Neural Network Study of Blasius Equation

In this work we applied a feed forward neural network to solve Blasius equation which is a third-order nonlinear differential equation. Blasius equation is a kind of boundary layer flow. We solved Blasius equation without reducing it into a system of first order equation. Numerical results are presented and a comparison according to some studies is made in the form of their results. Obtained results are found to be in good agreement with the given studies.

     

Synthesis of block copolymers by using polysilanes

Poly(methyl phenyl silane) was used to photoinitiate the polymerization of methyl methacrylate. Poly(methyl methacrylate) (PMMA), obtained this way, contains remaining polysilane chains. Photolysis of this PMMA in the presence of vinyl monomers such as styrene makes it possible to prepare block copolymers. Such PMMA prepolymers were also used to induce the polymerization of cyclohexene oxide through formation of PMMA-attached silyl radicals and subsequent oxidation to the corresponding ions in the presence of N-ethoxy-2-methylpyridinium hexafluorophosphate resulting in the formation of a block copolymer.     

Base‐catalyzed organosolv pulping of jute

Jute fibers were pulped using a novel caustic soda and ethanol–alkali (EtOH–NaOH) pulping process with the aim of minimizing problems associated with conventional chemical pulping. The effects of reaction conditions on the pulp yield, degree of delignification and selected physical properties were evaluated. The results indicate that adding ethanol to the conventional soda process was effective for improving both delignification and the physical properties of jute pulps. It was found that the delignification proceeded more rapidly and more selectively with ethanol–alkali than with alkali alone, giving higher yields at a given kappa number. Strength properties were markedly improved. The greatest values for burst (5.8 kPa m² g^?1) and tensile strength (68 N m g^?1) were obtained at 175 °C and 2.5 h reaction time. Copyright © 2003 Society of Chemical Industry     

Thermal inactivation kinetics of penicillin g acylase obtained from a mutant derivative of escherichia coli atcc 11105

Thermal inactivation kinetics of native and glutaraldehyde cross-linked forms of penicillin G acylase obtained from a mutant derivative of Escherichia coli ATCC 11105 were studied. Apparent activation energies for thermal inactivation of both native and cross-linked forms of enzyme were calculated to be [57-71 ± 8.46] and [67.11 ± 13.83] kcal mol^?1 respectively. This slight increase in activation energy-suggested that glutaraldehyde cross-linking did not markedly protect against thermal inactivation. Cross-linked enzyme did, however, have a significantly improved half-life at temperatures between 40°C and 50°C.     

Forecasting Bank Failure: Base Learners,Ensembles and Hybrid Ensembles

The prediction of bankruptcy for financial companies, especially banks, has been extensively researched area and creditors, auditors, stockholders and senior managers are all interested in bank bankruptcy prediction. In this paper, three common machine learning models namely Logistic, J48 and Voted Perceptron are used as the base learners. In addition, an attribute-base ensemble learning method namely Random Subspaces and two instance-base ensemble learning methods namely Bagging and Multi-Boosting are employed to enhance the prediction accuracy of conventional machine learning models for bank failure prediction. The models are grouped in the following families of approaches: (i) conventional machine learning models, (ii) ensemble learning models and (iii) hybrid ensemble learning models. Experimental results indicate a clear outperformance of hybrid ensemble machine learning models over conventional base and ensemble models. These results indicate that hybrid ensemble learning models can be used as a reliable predicting model for bank failures.     

Electrohydrodynamic Jet Printing Driven by a Triboelectric Nanogenerator

Electrohydrodynamic jet (e‐jet) printing is a high‐resolution printed electronics technique that uses an electric field to generate droplets. It has great application potential with the rapid development of flexible and wearable electronics. Triboelectric nanogenerators (TENG), which can convert mechanical motions into electricity, have found many high‐voltage applications with unique merits of portability, controllability, safety, and cost‐effectiveness. In this work, the application of a TENG is extended to printed electronics by employing it to drive e‐jet printing. A rotary freestanding TENG is applied as the high‐voltage power source for generating stable ink droplet ejection. The TENG‐driven droplet generation and ejection process and printed features with varied operation parameters are investigated. Results reveal that the jetting frequency could be controlled by the TENG's operation frequency, and high‐resolution printing with feature size smaller than nozzle size is achieved using the setup. Notably, TENG as the power source for e‐jet printing supplies a limited amount of current, which leads to better safety for both equipment and personnel compared to conventional high‐voltage power supplies. With the superiority of TENG in the sense of safety and cost, the work presents a promising solution for the next‐generation of high‐resolution printed electronics and broadens the scope of TENG application.     

Empirical model representing microbial activity in a submerged MBR treating strength wastewater

An expression for representing the microbial activity of biomass in the submerged membrane bioreactor treatingwhey was developed by considering the ratio of inert chemical oxygen demand (COD) in the effluent to that in the influent (C₅/C₀) and the ratio of volatile suspended solids to the suspended solids (MLVSS/MLSS). Subsequently, two expressions for the effects of above parameters on the specific oxygen uptake rate were statistically determined using a mathematical model developed by the regression analysis technique, including either the power function or the exponential growth models.     

Goal emulation and planning in perceptual space using learned affordances

In this paper, we show that through self-interaction and self-observation, an anthropomorphic robot equipped with a range camera can learn object affordances and use this knowledge for planning. In the first step of learning, the robot discovers commonalities in its action-effect experiences by discovering effect categories. Once the effect categories are discovered, in the second step, affordance predictors for each behavior are obtained by learning the mapping from the object features to the effect categories. After learning, the robot can make plans to achieve desired goals, emulate end states of demonstrated actions, monitor the plan execution and take corrective actions using the perceptual structures employed or discovered during learning. We argue that the learning system proposed shares crucial elements with the development of infants of 7–10 months age, who explore the environment and learn the dynamics of the objects through goal-free exploration. In addition, we discuss goal emulation and planning in relation to older infants with no symbolic inference capability and non-linguistic animals which utilize object affordances to make action plans.     

Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction

Accurate prediction of high performance concrete (HPC) compressive strength is very important issue. In the last decade, a variety of modeling approaches have been developed and applied to predict HPC compressive strength from a wide range of variables, with varying success. The selection, application and comparison of decent modeling methods remain therefore a crucial task, subject to ongoing researches and debates. This study proposes three different ensemble approaches: (i) single ensembles of decision trees (DT) (ii) two-level ensemble approach which employs same ensemble learning method twice in building ensemble models (iii) hybrid ensemble approach which is an integration of attribute-base ensemble method (random sub-spaces RS) and instance-base ensemble methods (bagging Bag, stochastic gradient boosting GB). A decision tree is used as the base learner of ensembles and its results are benchmarked to proposed ensemble models. The obtained results show that the proposed ensemble models could noticeably advance the prediction accuracy of the single DT model and for determining average determination of correlation, the best models for HPC compressive strength forecasting are GB–RS DT, RS–GB DT and GB–GB DT among the eleven proposed predictive models, respectively. The obtained results show that the proposed ensemble models could noticeably advance the prediction accuracy of the single DT model and for determining determination of correlation (R²_max), the best models for HPC compressive strength forecasting are GB–RS DT (R²=0.9520), GB–GB DT (R²=0.9456) and Bag–Bag DT (R²=0.9368) among the eleven proposed predictive models, respectively.     

High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform

This paper investigates the use of wavelet ensemble models for high performance concrete (HPC) compressive strength forecasting. More specifically, we incorporate bagging and gradient boosting methods in building artificial neural networks (ANN) ensembles (bagged artificial neural networks (BANN) and gradient boosted artificial neural networks (GBANN)), first. Coefficient of determination (R²), mean absolute error (MAE) and the root mean squared error (RMSE) statics are used for performance evaluation of proposed predictive models. Empirical results show that ensemble models (R²_BANN=0.9278, R²_GBANN=0.9270) are superior to a conventional ANN model (R²_ANN=0.9088). Then, we use the coupling of discrete wavelet transform (DWT) and ANN ensembles for enhancing the prediction accuracy. The study concludes that DWT is an effective tool for increasing the accuracy of the ANN ensembles (R²_WBANN=0.9397, R²_WGBANN=0.9528).