Determination of number of kanbans in a supply chain system via Memetic algorithm

In this research multi-stage supply chain system which is controlled by kanban system, is evaluated. In kanban system, decision making is based on determination of the number of kanbans as well as batch sizes. This paper attempts to model supply chain system with regard to costs under just-in-time (JIT) production philosophy. Since adopted model is of mixed integer non-linear programming (MINLP) type and solving it by exact algorithm such as branch and bound (B&B) takes a lot of time, a heuristic method via Memetic algorithm (MA) is presented. Some problems are solved by our proposed MA to illustrate its performance.     

Dimeric spectra analysis in Microsoft Excel: a comparative study

The purpose of this work is to introduce the reader to an Add-in implementation, Decom. This implementation provides the whole processing requirements for analysis of dimeric spectra. General linear and nonlinear decomposition algorithms were integrated as an Excel Add-in for easy installation and usage. In this work, the results of several samples investigations were compared to those obtained by Datan.     

A theory of mode of action of azolylalkylquinolines as DNA binding agents using automated flexible ligand docking

Azolylalkylquinolines (AAQs) are a family of quinolines with varying degrees of cytotoxic activity (comparable or moderately superior to adriamycin in some cases) developed in the past decade in our group where their exact mode of action is still unclear. In this study the most probable DNA binding mode of AAQs was investigated employing a novel flexible ligand docking approach by using AutoDock 3.0. Forty-nine AAQs with known experimental inhibitory activity were docked onto d(CGCAAATTTGCG)(2), d(CGATCG)(2) and d(CGCG)(2) oligonucleotides retrieved from the Protein Data Bank (PDB IDs: 102D, 1D12 and 1D32, respectively) as the representatives of the three plausible models of interactions between chemotherapeutic agents and DNA (groove binding, groove binding plus intercalation and bisintercalation, respectively). Good correlation (r(2)=0.64) between calculated binding energies and experimental inhibitory activities was obtained using groove binding plus intercalation model for phenyl-azolylalkylquinoline (PAAQ) series. Our findings show that the most probable mode of action of PAAQs as DNA binding agents is via intercalation of quinolinic moiety between CG base pairs with linker chain and azole moiety binding to the minor groove.     

Selection of regression models for predicting strength and deformability properties of rocks using GA

Recently, many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties. Although statistical analysis is a common method for developing regression models, but still selection of suitable transformation of the independent variables in a regression model is difficult. In this paper, a genetic algorithm (GA) has been employed as a heuristic search method for selection of best transformation of the independent variables (some index properties of rocks) in regression models for prediction of uniaxial compressive strength (UCS) and modulus of elasticity (E). Firstly, multiple linear regression (MLR) analysis was performed on a data set to establish predictive models. Then, two GA models were developed in which root mean squared error (RMSE) was defined as fitness function. Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.     

Non-parametric estimate of the system function of a time-varying system

The task of identifying an unknown dynamic system is made easier with prior knowledge on its behaviour. Using a frequency domain approach, the non-parametric maximum likelihood estimator of the system function, associated with the time-dependent impulse response of a time-varying system, is constructed. This is accomplished by the use of a simple linear least squares fitting algorithm, applied to the spectral response of the system to a multisine excitation. The noise variance on the system function is estimated simultaneously, and modelling errors can be detected, as illustrated on a simulation example.     

Combined quasi-static backward modeling and look-ahead fuzzy control of vehicles

Vehicle modeling can play an important role in vehicle power train design, control and energy management investigation. This paper presents a method for vehicle power train modeling. The key feature of the method is its presentation of the dynamic of vehicle based on the road information. This ability makes the method suitable for look-ahead energy management and fuel economy optimal control problems. With the aid of a road slope database, road geometry ahead of the vehicle is extracted. A fuzzy controller is developed that receives this information and controls the velocity of the vehicle with respect to its fuel consumption. In order to maintain the operation of the combustion engine near its efficient region, the fuzzy controller commands a continuously variable transmission. Simulations are carried out using real road data. The results are presented and discussed.     

Efflorescence control in geopolymer binders based on natural pozzolan

This paper addresses methods to reduce efflorescence in a geopolymer binder based on a pumice-type natural pozzolanic material from Taftan, Iran. Geopolymer pastes samples are analyzed for compressive strength and efflorescence formation after curing at 95% humidity for 28 days. To reduce the extent of efflorescence, Al-rich mineral admixtures such as metakaolin, ground granulated blast-furnace slag, and three types of calcium aluminate cements are incorporated into the dry binder at a range of concentrations. Hydrothermal curing at elevated temperatures also shows a positive effect in efflorescence reduction. Calcium aluminate cements show the greatest effect in efflorescence reduction, which is attributed to their dissolution in alkaline media releasing high amounts of alumina into the aluminosilicate geopolymer gel. These results confirm that it is possible to develop a more reliable geopolymer binder with improved properties either by adding a suitable amount of active alumina to precursors such as natural pozzolan, or by manipulating the curing conditions to enhance alumina release from less-reactive precursor phases.     

Polyaniline/akaganéite nanocomposite for detoxification of noxious Cr(VI) from aquatic environment

In this work, a removal of toxic hexavalent chromium Cr(VI) ion from aqueous solution was investigated and studied using nanocomposite of polyaniline (PANI) and akaganéite nanoparticles (NP). HCl doped PANI, and akaganéite NPs were prepared by chemical oxidative polymerization and co-precipitation techniques, respectively. The synthesized materials were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscope (SEM), and high-resolution transmission electron microscope (HRTEM). It was indicated that the formed oxide NPs were consisted of akaganéite as dominant phase plus minor phases of hematite, magnetite, and/or maghemite. HRTEM images of the prepared nanocomposite demonstrated that the phases of oxide NPs embedded in the nanocomposite had the same crystallinity and morphology of pristine oxide NPs. It was found that size of nanocomposite particles has diameter ranged from 8.95 to 16.21 nm. Cr(VI) removals in a wide pH range from 2 to 9 were appropriated for prepared nanocomposite. The nanocomposite has demonstrated high removal percentage of 99.2 % and removal capacity of 17.36 mg/g for 7.0 mg/L Cr(VI) polluted aqueous solution at pH 2.0 for 5-min contact time. The synthesized nanocomposite was applied to remove Cr(VI) from a leather tanning wastewater sample with efficiency of 93.4 %.     

Modeling and Optimization of SE and SI of Copper Flotation via Hybrid GA–ANN

In the present paper, a new attitude has been proposed for optimization of the separation efficiency (SE) and the Gaudin’s selectivity index (SI) in a flotation process by Hybrid artificial neural network (ANN) and genetic algorithm (GA). The chemical reagent’s dosage (collector, frother and fuel oil), pH, solid percentage, feed rate, Cu, Mo, and Fe grades in the flotation feed were selected as input variables and the SE-Cu and SI-Mo and SI-Fe were selected as output ones. Multilayer NN with back propagation (BP) algorithm was trained by the standard Bayesian regulation algorithm in which the validation data set did not required to be apart from its training. This algorithm with four-layer was used to relate output and input variables. Employment of Hybrid GA–ANN method resulted in significant improvement on GA fitnesses, as SE-Cu = 88, SI-Mo = 4.47 and SI of Fe = 12.85 were achieved. The input parameters corresponding to the fitnesses were as follows: pH = 12.25; the grade of Cu = 0.55%, Mo = 0.04% and Fe = 5.53%; the collector, frother and fuel–oil concentrations being 16.55, 15.54 and 2.71 (g/ton), respectively; the solid percentage was 25.84% and feed rate was 38,380 ton/day. The best fitness of GA was obtained after 10 generations by MSE value of 2.23.