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.     

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     

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.     

牛奶蛋白纤维(第Ⅰ部分)

牛奶蛋白纤维是一种新颖的工业生产纤维,中国已拥有2项发明专利.2004年它就已经通过了国际生态纺织品Oeko-Tex标准100认证.牛奶蛋白纤维是利用新型生物工程技术将牛奶去水脱脂,加工成适用于湿法纺丝工艺的蛋白浆后制得的纤维.它对皮肤友好,手感舒适,色彩鲜艳,具有很好的可染性.牛奶蛋白纤维可以纯纺或与羊绒、蚕丝、棉、毛、麻及其他纤维混纺.     

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.     

Preparation,characterization and pharmacokinetic evaluation of rosuvastatin calcium incorporated cyclodextrin-polyanhydride nanoparticles

Abstract

Objective: The aim of the study was to formulate, cyclodextrin (CD)-polyanhydride (PA) nanoparticles (CPNs) with rosuvastatin calcium (RCa) in order to enhance the poor oral bioavailability.

Methods: CPNs containing RCa/CD complexes were prepared by a modified solvent displacement method and morphological analyses, particle size (PS), polydispersity index (PDI), zeta potential (ZP), encapsulation efficiency (EE), DSC, FT-IR, XRD, ¹H-NMR analyses were performed. In vitro release properties, release kinetics, cytotoxicity, in vitro permeability and pharmacokinetic studies were also studied. The stability of the formulations were evaluated during the storage period of 3?months.

Results: The physicochemical studies showed that the RCa/CD complexes were well incorporated into CPNs resulted in nanosized particles (215.22 and 189.13?nm) with homogenous size distribution (PDI: 0.203 and 0.182) with relatively high incorporation capacity (76.11 and 68.18%) for the CPN1 and CPN2 formulations respectively. Sustained release of RCa from CPNs were achieved. The cytotoxicity values showed that the safety of the formulations. According to permeability studies, pure RCa had lowest permeability data (3.08?×?10^?7?cm?s^?1 P_app value) while CPNs gained higher permeability data (1.36?×?10^?5 and 1.12?×?10^?5?cm?s^?1 P_app values) for the CPN1 and CPN2 formulations respectively. CPN2 formulation was selected for pharmacokinetic studies and analyses results demonstrated that approximately 8-fold relative oral bioavailability enhancement compared to the pure RCa was achieved.

Conclusion: Considering the analyses results of the study, CPNs can be regarded as suitable, safe, functional oral delivery systems for RCa with enhanced oral bioavailability.