Determination of overall mass transfer coefficients in fixed bed sorption columns

In this study, a simple and effective technique for determining overall mass transfer coefficients in fixed bed sorption columns with recycle is presented. This new technique is free of the assumption of negligible time rate of solute concentration change in the fluid, i.e. (?c/?t) = 0, which has been widely used in former fixed bed sorption column analyses.     

Training Sequence Aided Signature Waveform Estimation

In code division multiple access channels multiuser detection techniques are known to be effective strategies to counter the presence of multiuser interference towards improving spectral efficiency. Generally, multiuser detectors can provide excellent performance only when the signature waveforms of all users are precisely known. Hence, the estimation of signature waveforms is a challenging issue in mobile communication systems. In this paper, we compare the performance of two short training sequence aided signature waveform estimators. One is maximum likelihood type signature waveform estimator that requires the knowledge of spreading sequences and short training sequences. The other estimator is recently proposed based on subspace method and requires the knowledge of training sequences only. Through the simulations, we show the signature waveform estimation performance of both systems and the effect of the estimation error on the performance of a multiuser detector. The complexity comparisons of both systems are also given. We use the term “signature waveform” to refer to the convolution of the channel and the spreading code throughout the paper. Hasan AMCA was born in 1961 in Nicosia-Cyprus. He graduated from the Higher Technological Institute in Magosa – Cyprus (which is renamed later as Eastern Mediterranean University). He joined EMU in 1985 after receiving a M.Sc. (Digital Signal Processing) degree from the University of Essex in England (1985). He took his Ph.D. (Mobile Communications) from the University of Bradford where he was on a Commonwealth scholarship. He has been teaching in the Electrical and Electronic Engineering Department of Eastern Mediterranean University since 1993 where he also served as the Vice Chairman from Spring 1998 to Spring 2000. He has been appointed as the Director of the School of Computing and Technology of the EMU since Spring 2000. His research interests include Multi User Detection of CDMA signals, Adaptive Equalisation, Multi Carrier Systems, Mobile Radio Systems and Networks, Internet and Information Technology Applications in Education. Ahmet Rizaner was born in Larnaca, Cyprus, on January 31, 1974. He received the B.S. and M.S. degrees in Electrical and Electronics Engineering from the Eastern Mediterranean University, Famagusta, North Cyprus, in 1996 and 1998, respectively. He completed his PhD. degree in Electrical and Electronic Engineering in Eastern Mediterranean University and joined Eastern Mediterranean University as a lecturer in 2004. He is lecturing in the School of Computing and Technology. His main research interests include CDMA communications, adaptive channel estimation, and multiuser detection technique. Kadri Hacioğlu was born in Nicosia, Cyprus. He received the B.Sc., M.Sc., and Ph.D. degrees in electrical and electronic engineering from the Middle East Technical University, Ankara, Turkey, in 1980, 1984, and 1990, respectively. After his two-year military service, in 1992, he joined the faculty of Eastern Mediterranean University, Magosa, North Cyprus, as an Assistant Professor, and became an Associate Professor in 1997. While there, he taught several classes on electronics, digital communications, speech processing and neural networks. During this time, he conducted research on applying fuzzy logic, neural networks, and genetic algorithms to signal processing and communications problems. From 1998 to 2000, he was a Visiting Professor in the Department of Computer Science, University of Colorado, Boulder. Here, he taught classes on neural networks and continued his research. Since 2000, he has been a Research Associate at the Center for Spoken Language Research, University of Colorado. He has authored or coauthored numerous papers and supervised a dozen M.Sc./Ph.D. theses. His current research interests are concept-based language modeling, speech understanding, natural language generation, and search methods in speech recognition/understanding. He also does research on multiuser detection and equalization in CDMA systems. Ali Hakan Ulusoy was born in Eskişehir, Turkey, on June 3, 1974. He graduated from the double major program of the department of Electrical and Electronic Engineering and department of Physics in Eastern Mediterranean University as the first rank student of Faculty of Engineering in 1996. He received his M.S. degree in Electrical and Electronic Engineering in Eastern Mediterranean University in 1998. He completed his Ph.D. degree in Electrical and Electronic Engineering in Eastern Mediterranean University and joined Eastern Mediterranean University as a lecturer in 2004. He is lecturing in the School of Computing and Technology. His current research interests include receiver design, multi-user detection techniques, blind and trained channel estimation in Code Division Multiple Access (CDMA).     

The construction productivity debate and the measurement of service qualities

Since the 1960s, researchers have provided short-term and long-term explanations for low productivity growth in the construction industry. In retrospect, the main challenge appears to be the measurement of changes in heterogeneous input and output qualities. The aim here is to review earlier construction productivity research and to compare it with more recent approaches to quality measurement used when analysing services productivity, ultimately intending to provide guidance for using performance data from construction projects. Relying on the EU KLEMS database, industries with similar patterns of productivity growth are identified, primarily the business services industry. In services productivity analyses, the attempts to introduce output quality measures reflecting customer satisfaction are particularly interesting, as this creates a link to productivity effects on clients. A conclusion is that it should be possible to use the increasing volume of performance indicator data collected for construction project benchmarking for extending the range of output quality variables. However, resource constraints imply that it is infeasible to base industry productivity statistics on project level data reflecting customer satisfaction and customer productivity effects.     

Comparative catalytic study on the carbothermic formation of hexagonal boron nitride with Li, Na, K and Ca carbonates

Catalytic effects of Li, Na, K and Ca carbonates on carbothermic formation of hexagonal boron nitride (hBN) were investigated. Experiments were conducted by keeping the plain and catalyst added B₂O₃ + C powder mixtures at 1400 °C in flowing nitrogen for 40–160 min. Products were subjected to quantitative and X-ray diffraction analyses, scanning and transmission electron microscopy examinations, and particle size measurements. Investigated catalysts increased the amount, particle size and crystallinity of the formed hBN. Average particle sizes of the obtained hBN powders were in 150–350 nm range. Catalytically, lithium and sodium carbonates were found to be the most effective, whereas calcium carbonate was the least effective. In the experiments conducted for 80 min, the quantity of the formed hBN increased 13-fold when 40 wt% Li₂CO₃ was used, as compared to plain mixture. The increases were ninefold and fourfold for the same duration when 40 wt% K₂CO₃ and 10 % CaCO₃ were used, respectively.     

Performance and exhaust emissions of a biodiesel engine

In this study, the applicabilities of Artificial Neural Networks (ANNs) have been investigated for the performance and exhaust-emission values of a diesel engine fueled with biodiesels from different feedstocks and petroleum diesel fuels. The engine performance and emissions characteristics of two different petroleum diesel-fuels (No. 1 and No. 2), biodiesels (from soybean oil and yellow grease), and their 20% blends with No. 2 diesel fuel were used as experimental results. The fuels were tested at full load (100%) at 1400-rpm engine speed, where the engine torque was 257.6 Nm. To train the network, the average molecular weight, net heat of combustion, specific gravity, kinematic viscosity, C/H ratio and cetane number of each fuel are used as the input layer, while outputs are the brake specific fuel-consumption, exhaust temperature, and exhaust emissions. The back-propagation learning algorithm with three different variants, single layer, and logistic sigmoid transfer function were used in the network. By using weights in the network, formulations have been given for each output. The network has yielded R² values of 0.99 and the mean % errors are smaller than 4.2 for the training data, while the R² values are about 0.99 and the mean % errors are smaller than 5.5 for the test data. The performance and exhaust emissions from a diesel engine, using biodiesel blends with No. 2 diesel fuel up to 20%, have been predicted using the ANN model.     

The optimization of welding parameters for friction stir spot welding of high density polyethylene sheets

Friction stir spot welding parameters affect the weld strength of thermoplastics, such as high density polyethylene (HDPE) sheets. The strength of a friction stir spot weld is usually determined by a lap-shear test. For maximizing the weld strength, the selection of welding parameters is very important. This paper presents an application of Taguchi method to friction stir spot welding strength of HDPE sheets. An orthogonal array, the signal to noise ratio (S/N), and the analysis of variance (ANOVA) are employed to investigate friction stir welding parameter effects on the weld strength. From the ANOVA and the S/N ratio response graphs, the significant parameters and the optimal combination level of welding parameters were obtained. Experimental results confirmed the effectiveness of the method.     

Synthesis and Electrochemical Properties of Carbon-Mixed LiEr_(0.02)Fe_(0.98)PO_4 Cathode Material for Lithium-ion Batteries

LiEr0.02Fe0.98PO4/C composite cathode was synthesized by a simple solution method with polyethylene glycol (PEG) as the reductive agent and carbon source. The effect of erbium doping on the electrochemical behavior of LiFePO4 was studied in this paper. The samples were characterized by X-ray powder diffraction and scanning electron microscopy and the electrochemical properties were investigated by the charge-discharge test. An initial discharge capacity of 149 mAh·g-1 was achieved for the LiEr0.02Fe0.98PO4/C composite cathode with a rate of 0.1 C. The electronic conductivity of Er doped LiFePO4/C was measured as 10-2 S·cm-1. The results indicated that erbium doping did not destroy the lattice structure of LiFePO4 and enlarge the lattice volume. These changes are beneficial to the improvement of the electrochemical performance of the LiFePO4 cathode.     

Wind power energy potential at the northeastern region of Saudi Arabia

In the present study the energy potential of wind for the Eastern Province of Saudi Arabia is investigated. A suitable Weibull distribution is generated based on the data obtained for a duration of one complete year at a costal location in northeastern Saudi Arabia. Comparison of this model is made with the Rayleigh distribution of wind power densities. Two horizontal-axis type of wind energy conversion systems which operate at fixed rpm are considered and a model of quadratic power output function is used. It is found that the error in using the Rayleigh approximation will be less than 10% of the full rated power density level.