Application of neural networks to heuristic scheduling algorithms

This paper considers the use of artificial neural networks (ANNs) to model six different heuristic algorithms applied to the n job, m machine real flowshop scheduling problem with the objective of minimizing makespan. The objective is to obtain six ANN models to be used for the prediction of the completion times for each job processed on each machine and to introduce the fuzziness of scheduling information into flowshop scheduling. Fuzzy membership functions are generated for completion, job waiting and machine idle times. Different methods are proposed to obtain the fuzzy parameters. To model the functional relation between the input and output variables, multilayered feedforward networks (MFNs) trained with error backpropagation learning rule are used. The trained network is able to apply the learnt relationship to new problems. In this paper, an implementation alternative to the existing heuristic algorithms is provided. Once the network is trained adequately, it can provide an outcome (solution) faster than conventional iterative methods by its generalizing property. The results obtained from the study can be extended to solve the scheduling problems in the area of manufacturing.     

DC Magnetic Properties of the Ho doped ZnO Compounds

In this work, we report on the DC magnetic properties of Ho doped ZnO, Zn_0.8?4x Ho _x O _y (0.05≤x≤0.10), polycrystalline samples prepared by the so-called solid state reaction method. Magnetic measurements, M–H and M–T, done by using a PPMS magnetometer, show a paramagnetic behavior including indirect antiferromagnetic coupling between some Ho³⁺ magnetic moments. In other words, the negative values of the Curie–Weiss temperatures, θ, for the polycrystalline samples with six different Ho concentrations indicate that there is an effective antiferromagnetic coupling among the magnetic moments of Ho³⁺ ions. However, the decrease in the magnitude of θ with increasing x indicates that this coupling decreases with increasing amount of Ho in the compounds. Furthermore, the effective magnetic moments, μ _eff, per Ho atom calculated from the 1/χ?T curves gradually increase with increasing concentration. Therefore, the trends of the magnitudes of θ, and μ _eff values clearly indicate that apart from the antiferromagnetic coupling there is also an increasing contribution of a ferromagnetic coupling with increasing x. Hence, one may also conclude that the compounds studied seem to have a kind of spin-glass behavior.     

Empyema thoracis--a series of 230 cases

A retrospective analysis of 230 cases of empyema thoracis which occurred in our clinic during the last 14 years have been presented. The causes, the pathogen organism, treatment with antibiotic are enumerated. The surgical treatment carried in resistant cases have been described Ten patients (4.34 percent) died due to respiratory failure and septic shock. Importance of early and appropriate treatment has been stressed.     

Electrochemical treatment of Baker's yeast wastewater containing melanoidin: optimization through response surface methodology

Effluents from Baker's yeast production plant contain a high percentage of color and a large amount of organic load. In the present study, Baker's yeast wastewater (BYW) is treated with the electrocoagulation (EC) process using Al electrodes. Operating parameters (pH, current density, color intensity and operating time) are optimized by response surface methodology (RSM). Quadratic models are developed for the responses which are removal efficiencies of color, chemical oxygen demand (COD) and total organic carbon (TOC) and operating cost (OC). Optimum operating parameters and responses are determined as initial pH 5.2, current density of 61.3 A/m(2) and operation time of 33 min, and 71% of color, 24% of COD, 24% of TOC removal efficiencies and OC of 0.869 €/m(3), respectively. The quadratic model fits for all responses very well with R(2) (>0.95). This paper clearly shows that RSM is able to optimize the operating parameters to maximize the color, COD and TOC removal efficiencies and minimize the OC.     

Super-resolution reconstruction of faces by enhanced global models of shape and texture

We present a computationally efficient method for the super-resolution reconstruction of face images from their low-resolution versions. It is based on generative models and utilizes both the shape and texture components together. The main idea is that the image details can be synthesized by global modeling of accurately aligned local image regions. In order to achieve sufficient accuracy in alignment, shape reconstruction is considered as a separate problem and solved together with texture reconstruction in a coordinated manner. Meanwhile, the statistical dependency between the shape and texture components is also considered. Moreover, different from traditional model-based super-resolution methods, we use a corrected form of the degradation operator with the aligned images. We show that when the degradation is used with the aligned texture components as is, it causes bias in the reconstructions. To overcome this problem, we reflect the same processing performed in alignment onto the degradation operator and use this corrected version in texture reconstruction. Experimental results show that the proposed solution provides superior image reconstructions (both qualitatively and quantitatively) in a faster way.     

Plant intelligence based metaheuristic optimization algorithms

Classical optimization algorithms are insufficient in large scale combinatorial problems and in nonlinear problems. Hence, metaheuristic optimization algorithms have been proposed. General purpose metaheuristic methods are evaluated in nine different groups: biology-based, physics-based, social-based, music-based, chemical-based, sport-based, mathematics-based, swarm-based, and hybrid methods which are combinations of these. Studies on plants in recent years have showed that plants exhibit intelligent behaviors. Accordingly, it is thought that plants have nervous system. In this work, all of the algorithms and applications about plant intelligence have been firstly collected and searched. Information is given about plant intelligence algorithms such as Flower Pollination Algorithm, Invasive Weed Optimization, Paddy Field Algorithm, Root Mass Optimization Algorithm, Artificial Plant Optimization Algorithm, Sapling Growing up Algorithm, Photosynthetic Algorithm, Plant Growth Optimization, Root Growth Algorithm, Strawberry Algorithm as Plant Propagation Algorithm, Runner Root Algorithm, Path Planning Algorithm, and Rooted Tree Optimization.     

Fe3O4 nanoparticle decorated novel magnetic metal oxide microcomposites for the catalytic degradation of 4-nitrophenol for wastewater cleaning applications

Journal of Materials Science: Materials in Electronics - Co3O4, NiO and ZnO microparticles were decorated with Fe3O4 nanoparticles where novel Co3O4@Fe3O4, NiO@Fe3O4 and ZnO@Fe3O4 microcomposites...     

The Relative Importance of Usability and Functionality Factors for E‐Health Web Sites

With the growing usage of the Internet, the demand for online health care information and advice as well as the number of health‐related Web sites are increasing. In case of online health information and advice, the user interface replaces face‐to‐face communication. To ensure that the users' needs are met, it is critical to balance functionality and usability in the design of the Web site. The present study seeks to identify the complex interrelationships among the various factors of usability and functionality concerning e‐health Web sites. Two Turkish e‐health Web sites were assessed for evaluation in this study. The findings show that the users of the health information Web sites give a higher priority to functionality and its factors, whereby the highest relative importance is on “services/facilities” and “personalization/categorization of information.” The most important usability factors related to the e‐health Web sites are “memorability” and “interaction.” © 2012 Wiley Periodicals, Inc.     

UV-induced synthesis of chitosan-<Emphasis Type="Italic">g</Emphasis>-polyacrylamide semi-IPN superabsorbent hydrogels

Superabsorbent hydrogels of chitosan-g-polyacrylamide with N,N′-methylene-bis-acrylamide as a crosslinker were prepared via UV irradiation in the absence of photoinitiator under homogeneous conditions. The product was characterized by Fourier transform infrared spectroscopy, differential scanning calorimetry, and scanning electron microscopy to confirm the formation of hydrogels. The transparent hydrogels have been observed to exhibit as much as 2987% swelling in acidic solution. In addition, the hydrogel which hydrolyzed for 6 h (0.24 × 10³ min) can have a water uptake of 106 times its weight (5300% swelling for 0.5 g hydrogel). The effect of several variables such as time, temperature, pH, acrylamide/chitosan ratio, crosslinker amount, and different media was explored. Finally, the prepared hydrogel have been used in adsorption of Zn(II) ions from water with high removal efficiency (0.636 meq g⁻¹ or 20.8 mg g⁻¹) at pH = 7. The experimental data of the adsorption equilibrium from Zn(II) solution fit well with the pseudo-second-order model.