An improved robust buffer allocation method for the project scheduling problem

Unpredictable uncertainties cause delays and additional costs for projects. Often, when using traditional approaches, the optimizing procedure of the baseline project plan fails and leads to delays. In this study, a two-stage multi-objective buffer allocation approach is applied for robust project scheduling. In the first stage, some decisions are made on buffer sizes and allocation to the project activities. A set of Pareto-optimal robust schedules is designed using the meta-heuristic non-dominated sorting genetic algorithm (NSGA-II) based on the decisions made in the buffer allocation step. In the second stage, the Pareto solutions are evaluated in terms of the deviation from the initial start time and due dates. The proposed approach was implemented on a real dam construction project. The outcomes indicated that the obtained buffered schedule reduces the cost of disruptions by 17.7% compared with the baseline plan, with an increase of about 0.3% in the project completion time.     

Characterization of the Influence of Tool Pin Profile on Microstructural and Mechanical Properties of Friction Stir Welding

In this study, the effect of tool pin profile on mechanical properties, microstructural, material flow, thermal and strain distributions of friction stir welding of AA5083 was investigated. Two different tools with cylindrical and square pin profiles were employed to produce the welds. A numerical model is developed for investigating the effect of tool pin profiles on material flow, thermal and strain distributions based on thermo-mechanically coupled rigid-viscoplastic 3D FEM. Then, optical microscopy was employed to characterize the microstructures features of the weld. Finally, tensile test was carried out to characterize the mechanical properties of the weld. Obtained results showed that square pin profile produced finer grain structure and higher ultimate strength relative to cylindrical one. These results may be related to higher eccentricity, larger stirred zone, and higher temperature in the weld zone of the square pin profile.     

Application of graphite screen printed electrode modified with dysprosium tungstate nanoparticles in voltammetric determination of epinephrine in the presence of acetylcholine

The current work focuses on the development of a sensitive and selective electrochemical device based on a graphite screen printed electrode modified with Dy_2(WO_4)_3 nanoparticles(DWO/SPE) for the analysis of epinephrine in samples also containing acetylcholine. The study proves that the sensor has excellent electron-mediating behavior in the oxidation of epinephrine in a 0.1 mol/L phosphate buffer solution(PBS)(pH 7.0). The application of the DWO/SPE in differential pulse voltammetry(DPV) is found to lead to distinct response for the oxidation of epinephrine and acetylcholine, with the potentials of the epinephrine and acetylcholine peaks(△E_p) to be 550 mV apart. The detection limits of the method for epinephrine and acetylcholine are 0.5 and 0.7 μmol/L(S/N = 3) and the responses are found to be linear in the concentration ranges of 1.0-900.0 μmol/L and 1.0-1200.0 μmol/L in a PBS buffer(pH = 7.0)respectively. The modified electrode was used for the detection of epinephrine and acetylcholine in real samples and found to produce satisfactory results. These results can be a proof that Dy_2(WO_4)_3 nanoparticles can find promising applications in electrochemical sensors to be used for the analysis of(bio)chemical species.     

Two-dimensional array self-assembled quantum dot sub-diffraction waveguides with low loss and low crosstalk

We model and demonstrate the behavior of two-dimensional (2D) self-assembled quantum dot (QD) sub-diffraction waveguides. By pumping the gain-enabled semiconductor nanoparticles and introducing a signal light, energy coupling of stimulated photons from the QDs enables light transmission along the waveguide. Monte Carlo simulation with randomized inter-dot separation reveals that the optical gain necessary for unity transfer is 3.1 × 10(7)?m(-1) for a 2D (2?μm length by 500?nm width) array compared to 11.6 × 10(7)?m(-1) for a 1D (2?μm length) given 8?nm diameter quantum dots. The theoretical results are borne out in experiments on 2D arrays by measurement of negligible crosstalk component with as little as 200?nm waveguide separation and is indicative of near-field optical coupling behavior. The transmission loss for 500?nm wide structures is determined to be close to 3?dB/4?μm, whereas that for 100?nm width is 3?dB/2.3?μm. Accordingly, higher pump power and gain would be necessary on the narrower device to create similar throughput. Considering existing nanoscale propagation methods, which commonly use negative dielectric materials, our waveguide shows an improved loss characteristic with comparable or smaller dimensions. Thus, the application of QDs to nanophotonic waveguiding represents a promising path towards ultra-high density photonic integrated circuits.     

Hybrid credit ranking intelligent system using expert system and artificial neural networks

The main goal of all commercial banks is to collect the savings of legal and real persons and allocate them as credit to industrial, services and production companies. Non repayment of such credits cause many problems to the banks such as incapability to repay the central bank’s loans, increasing the amount of credit allocations comparing to credit repayment and incapability to allocate more credits to customers. The importance of credit allocation in banking industry and it’s important role in economic growth and employment creation leads the development of many models to evaluate the credit risk of applicants. But many of these models are classic and are incapable to do credit evaluation completely and efficiently. Therefore the demand to use artificial intelligence in this field has grown up. In this paper after providing appropriate credit ranking model and collecting expert’s knowledge, we design a hybrid intelligent system for credit ranking using reasoning-transformational models. Expert system as symbolic module and artificial neural network as non-symbolic module are components of this hybrid system. Such models provide the unique features of each components, the reasoning and explanation of expert system and the generalization and adaptability of artificial neural networks. The results of this system demonstrate hybrid intelligence system is more accurate and powerful in credit ranking comparing to expert systems and traditional banking models.     

Perchlorate-selective membrane sensors based on two nickel-hexaazamacrocycle complexes

The perchlorate salts of nickel(II) complexes of 1,3,5,8,10,13-hexaazacyclotetradecane (1) and 1,8-tert-butyl-1,3,5,8,10,13-hexaazacyclotetradecane (2) were used in construction of PVC based membrane electrodes. These sensors show very good selectivity for ClO₄⁻ ions over a wide variety of anions. These electrodes exhibit Nernstian behavior with the slopes of 59.5 and 59.3 mV per decade for (1) and (2), respectively. The working concentration ranges of the sensors are 1.0 × 10⁻¹–9.0 × 10⁻⁷ M (1) and 1.0 × 10⁻¹–5.0 × 10⁻⁷ M (2) with the detection limits of 6.0 × 10⁻⁷ and 2.0 × 10⁻⁷ M, respectively. The response time of the both sensors is very fast, and can be used for 2 (I) and 12 (II) weeks in a pH range of 3.0–11.0. These electrodes were applied to the determination of perchlorate ions in wastewater and cattle urine samples.     

Metamodel-based collaborative optimization framework

This paper focuses on the metamodel-based collaborative optimization (CO). The objective is to improve the computational efficiency of CO in order to handle multidisciplinary design optimization problems utilising high fidelity models. To address these issues, two levels of metamodel building techniques are proposed: metamodels in the disciplinary optimization are based on multi-fidelity modelling (the interaction of low and high fidelity models) and for the system level optimization a combination of a global metamodel based on the moving least squares method and trust region strategy is introduced. The proposed method is demonstrated on a continuous fiber-reinforced composite beam test problem. Results show that methods introduced in this paper provide an effective way of improving computational efficiency of CO based on high fidelity simulation models.     

Minimizing total flow time for the non-permutation flow shop scheduling problem with learning effects and availability constraints

The flow shop scheduling problem is an attractive subject in the field of scheduling, which has attracted the attention of many researchers in the past five decades. In this paper, the non-permutation flow shop scheduling problem with the learning effects and machine availability constraints has been studied for minimizing the total flow time as a performance measure. First, a mixed integer linear programming model has been proposed for the modeling of the problem and then, an effective improving heuristic method, which is able to find proper non-permutation solutions, has been presented. Finally, the computational results are used for evaluation the performance and effectiveness of the proposed heuristic.     

Newtonian power curve and drop size distributions for vibromixers

Power input data are presented for a twin flat disk up-and-down moving (vibromixer) impeller operating in a small vessel with a range of Newtonian liquids. Vibromixer power number and Reynolds number are defined and are used to establish the Newtonian power curve for this type of mixer. Drop size distributions are presented for xylene-in-water dispersions under turbulent flow conditions in the vibromixer and are shown to vary with the maximum velocity of the disk (2πAf). The Sauter mean drop diameter of the distribution is related to the vibromixer Weber number, (We =ρ(2πAf)²D/σ), by an equation of the type d₃₂/D = C (We)^?3/5 with the coefficient C = 0.37.     

Emotion classification during music listening from forehead biosignals

Emotion recognition systems are helpful in human–machine interactions and clinical applications. This paper investigates the feasibility of using 3-channel forehead biosignals (left temporalis, frontalis, and right temporalis channel) as informative channels for emotion recognition during music listening. Classification of four emotional states (positive valence/low arousal, positive valence/high arousal, negative valence/high arousal, and negative valence/low arousal) in arousal–valence space was performed by employing two parallel cascade-forward neural networks as arousal and valence classifiers. The inputs of the classifiers were obtained by applying a fuzzy rough model feature evaluation criterion and sequential forward floating selection algorithm. An averaged classification accuracy of 87.05 % was achieved, corresponding to average valence classification accuracy of 93.66 % and average arousal classification accuracy of 93.29 %.