DETERMINATION OF COPPER(II) IN NATURAL WATERS BY EXTRACTION USING N-O-VANILLIDINE-2-AMINO-P-CRESOL AND FLAME ATOMIC ABSORPTION SPECTROMETRY

A simple and accurate solvent extraction method was developed for the separation and preconcentration of trace levels of copper(II) in water using N-o-vanillidine-2-amino-p-cresol prior to its determination using flame atomic absorption spectrometry. Analytical parameters such as the pH of the aqueous phase, extraction time, concentration of the reagent, concentration of the nitric acid back extraction agent, volumes of aqueous phase and back extraction agent, and concomitant ions on the extraction yield of the copper(II) were investigated and optimized. Under the optimal conditions, the detection limit (3σ) was 0.61 µ g L^?1 for a 25 mL blank solution, yielding a preconcentration factor of 12.5. The method was succesfully applied to the determination of copper(II) in tap water, river water, and seawater. The accuracy of the method was verified by analyzing two certified reference materials and spiked water samples. The results obtained were in agreement with the certified values and the recoveries for spiked water samples were between 98 and 112%.     

Benders Decomposition Algorithms for Two Variants of the Single Allocation Hub Location Problem

Networks and Spatial Economics - The hub location problem (HLP) is a special type of the facility location problem with numerous applications in the airline industry, postal services, and computer...     

Numerical Modeling of Exit-Ply Delamination During Drilling of CFRPs Considering Thermal Effects

Abstract

A finite element (FE) model for exit-ply delamination during drilling carbon fiber reinforced polymers (CFRPs) laminates is presented. The current FE model is developed to predict critical thrust force at the onset of delamination for 1 and 2 plies under the twist drill for various cutting temperatures. The interface behavior for delamination onset is modeled using surface based cohesive zone model (CZM). The numerical predictions for critical thrust force are compared with experimental thrust forces for various number of plies under the twist drill over a range of cutting temperature. Thrust force predictions were found to match with experimental data.     

Refinement of some basic features of Zr surface-layered Bi-2223 superconductor with diffusion annealing temperature

This study aims to investigate the influences of diffusion annealing temperatures on structural, morphological, electrical, and superconducting features of Zr surface-layered Bi-2223 ceramics. The present study also covers an in-depth understanding of correlations between disorders and transition temperatures. The Zr diffusion is carried out via an annealing process between 650 and 840 °C. The observed results depict that the Zr ions can easily diffuse into the deeper level of Bi-ceramics and possible Zr/Bi substitution has occurred due to the driving force of high thermal energy. Besides, it is found that the Zr diffusion improves the general crystallinity quantities of Bi-2223 ceramic up to 800 °C annealing temperature. In addition, better intergranular couplings with a smoother plate-like structure are extensively observed in surface morphology for the samples annealed at 800 °C. Significant refinements of both basic electrical resistivity, hole carrier densities, and critical temperatures with narrow transitions are also obtained for the Zr surface-layered Bi-2223 ceramics after the 800 °C annealing process. The obtained improvements in critical fundamental features can be attributed to the optimum pairing mechanism, best crystal structure quality, ideal Cu–O₂ interlayer coupling strengths, and enhanced interaction between adjacent superconductive layers. Besides, the first-order derivative of electrical resistivity versus temperature graphs indicates that the best annealing temperature enables to triggers to stabilize the superconductivity in the homogeneous regions. It can be concluded that the Zr impurity diffusion at 800 °C is promising for the improvement in the basic features of Bi-2223 superconducting systems for future applications in superconductor technology.

     

Learning-based symbolic assume-guarantee reasoning for Markov decision process by using interval Markov process

Many real-life critical systems are described with large models and exhibit both probabilistic and non-deterministic behaviour. Verification of such systems requires techniques to avoid the state space explosion problem. Symbolic model checking and compositional verification such as assume-guarantee reasoning are two promising techniques to overcome this barrier. In this paper, we propose a probabilistic symbolic compositional verification approach (PSCV) to verify probabilistic systems where each component is a Markov decision process (MDP). PSCV starts by encoding implicitly the system components using compact data structures. To establish the symbolic compositional verification process, we propose a sound and complete symbolic assume-guarantee reasoning rule. To attain completeness of the symbolic assume-guarantee reasoning rule, we propose to model assumptions using interval MDP. In addition, we give a symbolic MTBDD-learning algorithm to generate automatically the symbolic assumptions. Moreover, we propose to use causality to generate small counterexamples in order to refine the conjecture assumptions. Experimental results suggest promising outlooks for our probabilistic symbolic compositional approach.     

Passenger Flow Prediction Based on Newly Adopted Algorithms

Passenger flow forecasting is an essential part of transportation systems. Neural networks in the transportation field have been applied to passenger demand prediction. In this paper, we developed two hybrid methods, known as parlimentary optimization algorithm-artificial neural network (POA-ANN), and intelligent water drops algorithm-ANN (IWD algorithm-ANN). In addition, we applied the proposed algorithms to illustrate the effect of precise prediction for passenger queues. We mainly focus on predicting passenger demand by comparing the genetic algorithm-ANN (GA-ANN) with POA-ANN and IWD-ANN. The results of prediction methods suggest that both POA-ANN and IWD-ANN provide a better forecasting performance, which is obtained via mean square error (MSE), than GA-ANN in the field of passenger flow prediction. This study illustrates that the newly adopted algorithms exhibit good performance for passenger prediction.     

Alanine containing porous beads for mercury removal from artificial solutions

N‐methacryloyl‐(L )‐alanine (MALA) was synthesized by using methacryloyl chloride and alanine as a metal‐complexing ligand or comonomer. Spherical beads with an average diameter of 150–200 μm were obtained by suspension polymerization of MALA and 2‐hydroxyethyl methacrylate (HEMA) conducted in an aqueous dispersion medium. Poly(HEMA–MALA) beads were characterized by SEM, swelling studies, surface area measurement, and elemental analysis. Poly(HEMA–MALA) beads have a specific surface area of 68.5 m²/g. Poly(HEMA–MALA) beads with a swelling ratio of 63%, and containing 247 μmol MALA/g were used in the removal of Hg²⁺ from aqueous solutions. Adsorption equilibrium was achieved in about 60 min. The adsorption of Hg²⁺ ions onto PHEMA beads was negligible (0.3 mg/g). The MALA incorporation into the polymer structure significantly increased the mercury adsorption capacity (168 mg/g). Adsorption capacity of MALA containing beads increased significantly with pH. The adsorption of Hg²⁺ ions increased with increasing pH and reached a plateau value at around pH 5.0. Competitive heavy metal adsorption from aqueous solutions containing Cd²⁺, Cu²⁺, Pb²⁺, and Hg²⁺ was also investigated. The adsorption capacities are 44.5 mg/g for Hg²⁺, 6.4 mg/g for Cd²⁺, 2.9 mg/g for Pb²⁺, and 2.0 mg/g for Cu²⁺ ions. These results may be considered as an indication of higher specificity of the poly(HEMA–MALA) beads for the Hg²⁺ comparing to other ions. Consecutive adsorption and elution operations showed the feasibility of repeated use for poly(HEMA–MALA) chelating beads. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 1222–1228, 2006     

An investigation into design and manufacturing of mechanical conveyors systems for food processing

This paper presents the results of a research investigation undertaken to develop methodologies and techniques that will reduce the cost and time of the design, manufacturing and assembly of mechanical conveyor systems used in the food and beverage industry. The improved methodology for design and production of conveyor components is based on the minimisation of materials, parts and costs, using the rules of design for manufacture and design for assembly. Results obtained on a test conveyor system verify the benefits of using the improved techniques. The overall material cost was reduced by 19% and the overall assembly cost was reduced by 20% compared to conventional methods.     

Conception of Stretchable Resistive Memory Devices Based on Nanostructure‐Controlled Carbohydrate‐block‐Polyisoprene Block Copolymers

It is discovered that the memory‐type behaviors of novel carbohydrate‐block ‐polyisoprene (MH‐b ‐PI) block copolymers‐based devices, including write‐once‐read‐many‐times, Flash, and dynamic‐random‐access‐memory, can be easily controlled by the self‐assembly nanostructures (vertical cylinder, horizontal cylinder, and order‐packed sphere), in which the MH and PI blocks, respectively, provide the charge‐trapping and stretchable function. With increasing the flexible PI block length, the stretchability of the designed copolymers can be significantly improved up to 100% without forming cracks. Thus, intrinsically stretchable resistive memory devices (polydimethylsiloxane(PDMS)/carbon nanotubes(CNTs)/MH‐b ‐PI thin film/Al) using the MH‐b ‐PI thin film as an active layer is successfully fabricated and that using the MH‐b ‐PI_12.6k under 100% strain exhibits an excellent ON/OFF current ratio of over 10⁶ (reading at ?1 V) with stable V _set around ?2 V. Furthermore, the endurance characteristics can be maintained over 500 cycles upon 40% strain. This work establishes and represents a novel avenue for the design of green carbohydrate‐derived and stretchable memory materials.     

Novel hybrid block copolymer nanocarrier systems to load lipophilic drugs prepared by microphase inversion method

Nanoparticles based on block copolymers of oligosaccharides [β-cyclodextrin (βCyD) and maltoheptaose (Mal₇)] and poly(ε-caprolactone) (PCL) were prepared by microphase inversion method. Zeta-potential, particle size measurements and morphological analysis of drug-free and drug-loaded nanoparticles were performed by using, respectively, laser-doppler anemometry, dynamic and static light scattering and transmission electron microscopy. ρ-Ratio values were correlated with transmission electron microscopy observations. Both types of amphiphilic block copolymers, βCyD-b-PCL_5k and Mal₇-b-PCL_5k, self-assembled in water to form spherical vesicles, presented a hydrodynamic diameter of 72 and 34 nm, respectively. The incorporation of drugs into nanoparticles did not affect significantly the particle size for βCyD-b-PCL_5k-based nanoparticles with progesterone, unlike the other tested systems. On the other hand, all nanoparticles (with and without drug) were negatively charged. Both nanoparticulate systems showed high drug loading efficiency (higher than 95%), confirming their suitability as delivery system for lipophilic drugs.