Hygrothermal Fracture Analysis of Orthotropic Materials Using J k -Integral

A new computational method based on the J _k -integral is put forward for the purpose of conducting fracture analysis of orthotropic materials subjected to hygrothermal stresses. By utilizing the constitutive relations of plane orthotropic hygrothermoelasticity, an alternative expression for the J _k -integral is derived to replace the general limit definition. A numerical procedure is developed and integrated into a finite element analysis software to implement the proposed form of the J _k -integral. Temperature and specific moisture concentration fields, which are required in fracture calculations, are also computed through finite element analysis. Numerical results are generated by considering an embedded crack in a polymer matrix fibrous composite laminate, that is subjected to steady-state hygrothermal loading. Comparisons of the mixed-mode stress intensity factors computed by the J _k -integral based method to those evaluated via the displacement correlation technique demonstrate that, the proposed form of the J _k -integral is domain independent and leads to numerical results of high accuracy. Presented parametric analyses illustrate the influences of the fiber volume fraction and the crack location on the modes I and II stress intensity factors, the energy release rate, and the T-stress.     

Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycle system-34

Recent technical developments have made it possible to generate electricity from geothermal resources of low and medium enthalpy. One of these technologies is the Kalina Cycle System. In this study, electricity generation from Simav geothermal field was investigated using the Kalina cycle system-34 (KCS-34). However, the design of these technologies requires more proficiency and longer times within complex calculations. An artificial neural network (ANN) is a new tool used to make a decision for the optimum working conditions of the processes within the expertise. In this study, the back-propagation learning algorithm with three different variants, namely Levenberg-Marguardt (LM), Pola-Ribiere Conjugate Gradient (CGP), and Scaled Conjugate Gradient (SCG), were used in the network so that the best approach could be found. The most suitable algorithm found was LM with 7 neurons in a single hidden layer. The obtained weights were used in optimization process by coupling the life-cycle-cost concepts.     

Estimated anthropometric measurements of Turkish adults and effects of age and geographical regions

The purpose of this paper is to estimate the anthropometric characteristics of the Turkish population by geographical region, age and gender. A survey of 4205 samples consisting of 2263 male and 1942 female civilian subjects was done in the year of 2007. It contains data from all seven geographical regions of Turkey and from all age groups. In the study, height and weight of the subjects were measured by age. Then, these data were used for estimating anthropometric measurements of the Turkish population. The main result of this study is the anthropometric measurements table for Turkey which shows 37 measurements that are commonly used in industry. The anthropometric measurements that are used to make the designs fit human shape and the human/machine or human/environment interface should be updated through time due to changing body types over time (secular trends) due to numerous factors. In this study, we not only update these variables, which are 30 years old, but also investigate the age and region effects on stature and weight of Turkish population.Relevance to industry: During the design phase of a product, incorporating anthropometric information would yield more efficient designs, which are more user friendly, safer and enable higher performance and productivity. So the anthropometric measurements table that is extracted from this study can be used by industry to produce better human oriented products for Turkish population.     

Assessment of odorous VOCs released from a main MSW landfill site in Istanbul-Turkey via a modelling approach

An air pollution modeling study was conducted to investigate the odorous effects of volatile organic compounds (VOCs) emissions from a sanitary landfill area on ambient air quality. The atmospheric dispersion of hydrogen sulfide (H₂S) and 22 VOCs was modeled. Industrial Source Complex v3 Short Term (ISCST3) model was used to estimate hourly concentrations of odorous VOCs over the nearest residential area. Odor thresholds of VOCs of interest were also found in the literature. Results showed that short-term averages of three odorous VOCs, namely ethyl mercaptan, methyl mercaptan and hydrogen sulfide, exceeded their odor thresholds, which are reported to be 0.022, 0.138 and 11.1 μg/m³, respectively, at several points within the domain. Their highest concentrations within Gokturk County were estimated to be 0.09387 μg/m³ for ethyl mercaptan, 0.07934 μg/m³ for methyl mercaptan and 6.315 μg/m³ for hydrogen sulfide. Short-term model results revealed the occasional odor problems being reported for Gokturk County. Hourly concentrations were used to obtain frequencies of odor episodes in Gokturk County via a probability analysis. The results showed that ethyl mercaptan concentrations did not exceed its odor threshold during more than 8.84% of the time. Similarly, the maximum odor episode frequencies for methyl mercaptan and hydrogen sulfide were 0.98% and 0.34% of the time, respectively.     

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.     

IGNITION DELAY CHARACTERISTICS OF SOME TURKISH VEGETABLE OIL-DIESEL FUEL BLENDS

Vegetable oils have chances to be used in Diesel engines as alternative fuels contributing to the solution of some agricultural, environmental and economical problems. Direct use of them has some technical problem yet but as blended fuels with diesel fuel or esters they have places on the application area. In this paper the effect of the compression ratio on ignition delay is investigated in an ASTM-CER engine working with four different types of vegetable oil of Turkish origin (sunflower, corn, soybean, and olive oil) blended with grade No.2-D diesel fuel at a ratio of 20/80 (v/v) and the results are compared with baseline diesel fuel. Longer ignition delay periods have generally been obtained for blend fuels ranking from olive oil to sunflower oil as compared to diesel fuel.     

Castleman's disease. A case report

Angiofollicular lymph node hyperplasia or Castleman's Disease (CD) is a rare lymphoproliferative disorder that manifests itself as a local or generalized tumor-like condition affecting both lymph nodes and non-nodal tissues, usually in the chest and abdomen. Hyaline vascular and plasma cell types have been identified histologically. A new clinical form of CD with multisystemic involvement has been defined as multicentric Castleman's disease. It is very rare in childhood. In this paper we present an eight-year-old boy with multicentric Castleman's disease.