Extraction of caffeine from tea stalk and fiber wastes using supercritical carbon dioxide

The purpose of this study was to investigate obtaining of caffeine from tea plant wastes by supercritical carbon dioxide extraction. Experiments were carried out with tea stalk and fiber wastes of Turkish tea plants that has no economical value. Stalk and fiber wastes were supplied from tea factories. These wastes were ground, sieved and dried at 105 °C temperature in an oven. Parameters affecting caffeine leaching from tea wastes were determined to be, extraction time, extraction temperature, carbon dioxide flow rate, process pressure and particle size. The maximum yield of caffeine from tea stalk wastes and fiber wastes were 14.9 mg/g tea stalk and 19.2 mg/g tea fiber, respectively. The yield increase had been recorded as 61.9% and 65.5%, respectively, in comparison with the chloroform extraction of tea stalk and fiber wastes.     

Water-soluble poly(4-styrenesulfonic acid-co-maleic acid) stabilized ruthenium(0) and palladium(0) nanoclusters as highly active catalysts in hydrogen generation from the hydrolysis of ammonia–borane

Water-soluble poly(4-styrenesulfonic acid-co-maleic acid), PSSA-co-MA, stabilized ruthenium(0) and palladium(0) nanoclusters were for the first time prepared in situ from the reduction of ruthenium(III) chloride and potassium tetrachloropalladate(II), respectively, by ammonia–borane during its hydrolysis at room temperature. PSSA-co-MA stabilized ruthenium(0) and palladium(0) nanoclusters having average particle size of 1.9 ± 0.5 and 3.5 ± 1.6 nm, respectively, were isolated from the reaction solution and characterized by TEM and UV–visible electronic absorption spectroscopy. PSSA-co-MA stabilized ruthenium(0) and palladium(0) nanoclusters are highly active catalysts for hydrogen generation from the hydrolysis of ammonia–borane at low temperature. PSSA-co-MA stabilized ruthenium(0) and palladium(0) nanoclusters provide 51,720 and 8720 turnovers, respectively, in the hydrogen generation from the hydrolysis of ammonia–borane at 25 °C before deactivation. Catalytic hydrolysis of ammonia–borane is first order with respect to the catalyst concentration, but zero order with respect to the substrate concentration in the case of both ruthenium(0) and palladium(0) nanoclusters. Activation energies for the hydrolysis of ammonia–borane in the presence of PSSA-co-MA stabilized ruthenium(0) or palladium(0) nanoclusters (54 ± 2 kJ mol⁻¹ and 44 ± 2 kJ mol⁻¹, respectively) are smaller than most of the values reported for the same reaction in the presence of other catalyst systems.     

An integrated decision aid extension to material selection problem

Choice of an adequate material in design process is one of the critical tasks for the relevant decision-makers. At this insight, the consistency of the decisions is extremely depending on the relevance of adapted techniques to the nature of different problem cases. This paper proposed an integrated decision aid (IDEA) to match the suitable techniques with different problem cases based on the following six dimensions: (i) the type of the decision problem, (ii) the size of the problem, (iii) selection of the preference techniques by decision-makers, (iv) decision-makers’ preference structure, (v) the necessity for the use of relative importance, (vi) the nature of performance values. Furthermore, the implementation procedure of the proposed IDEA for a material selection problem is demonstrated with the previously cited applications from material science literature. Hereafter, it is expected that the IDEA provides great advantages and encouragements to researchers/practitioners in order to prevent excessive time consuming, probable misapplications, and the other challenging issues in multiple criteria analysis of material selection problems.     

Effect of nonlinearity on site response and ground motion due to earthquake excitation

Ground motions under earthquake loading are influenced by the local soil conditions, but the non-linearity of the soil behavior makes it difficult to estimate the site response. The paper reports a non-linear analysis developed to elucidate the effect of non-linearity on site response, illustrating its applicability with reference to a petroleum storage tank site subjected to earthquake excitation.     

Wavelet decomposition of cardiovascular signals for baroreceptor function tests in pigs

In this paper, the discrete wavelet transform (DWT) was applied to analyze the fluctuations in RR interval and systolic arterial pressure (SAP) recorded from eight alpha-chloralose anesthetized pigs. Our aim was to characterize the autonomic modulation before and after cardiac autonomic blockade and during baroreflex function tests. The instantaneous power of decomposed low-frequency (LF) and high-frequency (HF) components was used for a time-variant spectral analysis. Our results suggested that transient events and changes in autonomic modulation were detected with high temporal resolution. A nonlinear relationship between RR interval and SAP during pharmacologically induced changes in blood pressure was found, when the superimposed effect of respiratory sinus arrhythmia was removed. In addition, the baroslopes were nearly linear when both the LF and HF components were removed using DWT decomposition.     

Efficient string matching with wildcards and length constraints

This paper defines a challenging problem of pattern matching between a pattern P and a text T, with wildcards and length constraints, and designs an efficient algorithm to return each pattern occurrence in an online manner. In this pattern matching problem, the user can specify the constraints on the number of wildcards between each two consecutive letters of P and the constraints on the length of each matching substring in T. We design a complete algorithm, SAIL that returns each matching substring of P in T as soon as it appears in T in an O(n+klmg) time with an O(lm) space overhead, where n is the length of T, k is the frequency of P's last letter occurring in T, l is the user-specified maximum length for each matching substring, m is the length of P, and g is the maximum difference between the user-specified maximum and minimum numbers of wildcards allowed between two consecutive letters in P.SAIL stands for string matching with wildcards and length constraints. Gong Chen received the B.Eng. degree from the Beijing University of Technology, China, and the M.Sc. degree from the University of Vermont, USA, both in computer science. He is currently a graduate student in the Department of Statistics at the University of California, Los Angeles, USA. His research interests include data mining, statistical learning, machine learning, algorithm analysis and design, and database management. Xindong Wu is a professor and the chair of the Department of Computer Science at the University of Vermont. He holds a Ph.D. in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published extensively in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, IJCAI, AAAI, ICML, KDD, ICDM and WWW, as well as 12 books and conference proceedings. Dr. Wu is the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (by the IEEE Computer Society), the founder and current Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM),an Honorary Editor-in-Chief of Knowledge and Information Systems (by Springer), and a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He is the 2004 ACM SIGKDD Service Award winner. Xingquan Zhu received his Ph.D degree in Computer Science from Fudan University, Shanghai, China, in 2001. He spent 4 months with Microsoft Research Asia, Beijing, China, where he was working on content-based image retrieval with relevance feedback. From 2001 to 2002, he was a postdoctoral associate in the Department of Computer Science at Purdue University, West Lafayette, IN. He is currently a research assistant professor in the Department of Computer Science, the University of Vermont, Burlington, VT. His research interests include data mining, machine learning, data quality, multimedia computing, and information retrieval. Since 2000, Dr. Zhu has published extensively, including over 50 refereed papers in various journals and conference proceedings. Abdullah N. Arslan got his Ph.D. degree in Computer Science in 2002 from the University of California at Santa Barbara. Upon his graduation he joined the Department of Computer Science at the University of Vermont as an assistant professor. He has been with the computer science faculty there since then. Dr. Arslan's main research interests are on algorithms on strings, computational biology and bioinformatics. Dr. Arslan earned his Master's degree in Computer Science in 1996 from the University of North Texas, Denton, Texas and his Bachelor's degree in Computer Engineering in 1990 from the Middle East Technical University, Ankara, Turkey. He worked as a programmer for the Central Bank of Turkey between 1991 and 1994. Yu He received her B.E. degree in Information Engineering from Zhejiang University, China, in 2001. She is currently a graduate student in the Department of Computer Science at the University of Vermont. Her research interests include data mining, bioinformatics and pattern recognition.     

Transitional gate delay detection for combinational circuits usinga genetic algorithm

The authors present a new technique for the generation of test vector-pairs that detect both delay and single stuck-at-fault conditions in digital logic circuits. A genetic algorithm (GA), is used to pursue and extract efficient tests from a complex search space. Results obtained for the ISCAS 1985 benchmark circuits compare favourably with the results of other researchers, even when the genetic system considers both delay and single stuck-at-fault models