Comparison of different cultivation modes and light intensities using mono‐cultures and co‐cultures of Haematococcus pluvialis and Chlorella zofingiensis

BACKGROUND: Recent studies indicate that microalgal cultivation using organic carbon sources has the potential to provide high yields. Haematococcus pluvialis and Chlorella zofingiensis, two important carotenoid producers, were selected for co‐culture cultivations to utilize the unique advantages of both organisms. A co‐culture production process was investigated in terms of the effects of organic carbon source, co‐cultivation method, and light intensity on carotenoid production. RESULTS: The addition of 5 g L^?1 glucose resulted in a growth rate of 0.60 day^?1 for H. pluvialis and 0.59 day^?1 for C. zofingiensis, which were higher than those for other carbon sources tested and the control group. Incremental increase of light intensity instead of direct increase to 170 µE m^?2s^? prevented cell loss in both cultures. Co‐cultivation based on cell numbers (60% H. pluvialis and 40% C. zofingiensis) prevented population domination of one microalgae over the other. The biomass production rate of the co‐culture was higher (0.61 g L^?1 day^?1) in glucose‐enriched medium. The total carotenoid content of the co‐culture in the control culture was higher (0.83 mg total carotenoids g^?1 cell) than that obtained in glucose‐enriched medium (0.54 mg total carotenoids g^?1 cell) but not as high as the amounts reached in mono‐cultures. CONCLUSION: Total carotenoid content of the mono‐cultures gave higher yields in standard bold basal medium (BBM). Preliminary high performance liquid chromatography (HPLC) studies indicated a variation in the amounts of astaxanthin isomers produced. Further studies are in progress to determine the effects of carbon‐enriched media and co‐cultivation on the type of isomers and caretenoids produced. Copyright © 2010 Society of Chemical Industry     

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.     

Marble mosaic tiling automation with a four degrees of freedom Cartesian robot

The art of mosaic has arisen thousands of years ago. Despite all those years and all the attention it has received till today, mosaic tiling is still being carried out manually and mosaic tiling processes have never been changed except the tool and material developments.     

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     

Inferring Phylogenetic Trees Using Answer Set Programming

We describe the reconstruction of a phylogeny for a set of taxa, with a character-based cladistics approach, in a declarative knowledge representation formalism, and show how to use computational methods of answer set programming to generate conjectures about the evolution of the given taxa. We have applied this computational method in two domains: historical analysis of languages and historical analysis of parasite-host systems. In particular, using this method, we have computed some plausible phylogenies for Chinese dialects, for Indo-European language groups, and for Alcataenia species. Some of these plausible phylogenies are different from the ones computed by other software. Using this method, we can easily describe domain-specific information (e.g., temporal and geographical constraints), and thus prevent the reconstruction of some phylogenies that are not plausible. This paper is a revised and extended version of [3].