Quinoxaline derivatives as long wavelength photosensitizers in photoinitiated cationic polymerization of diaryliodonium salts

The present paper is focused on visible light initiated cationic polymerizations. Photoinitiated polymerization of representative vinyl ether and oxirane monomers using two quinoxaline derivatives; namely (2-(2,3-dihydrobenzo [b][1,4]dioxin-6-yl)-3-(2,3-dihydrobenzo[b]-[1,4]dioxin-7-yl)-5-(2,3-dihydrothieno[3,4-b][1,4]dioxin-5-yl)-8-(2,3-dihydrothieno[3,4-b][1,4]dioxin-7yl) quinoxaline) (DBQEd) and 2,3,5,8-tetra(thiophen-2-yl)quinoxaline (TTQ) are studied. Novel dyes based on the quinoxaline skeleton are employed as efficient photosensitizers in cationic photopolymerizations. Polymerizations were initiated at room temperature upon irradiation with long-wavelength UV and visible lights in the presence of diphenyliodonium hexafluorophosphate (Ph₂I⁺PF₆^?). The progress of the polymerizations was monitored by optical pyrometry (OP). Solar irradiation is also employed to carry out the cationic polymerization of a diepoxide monomer in the presence of air.     

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.     

Tribological Characteristics of Calophyllum inophyllum–Based TMP (Trimethylolpropane) Ester as Energy-Saving and Biodegradable Lubricant

The purpose of this research is an experimental study of Calophyllum inophyllum (CI)-based trymethylolpropane (TMP) ester as an energy-saving and biodegradable lubricant and compare it with commercial lubricant and paraffin mineral oil using a four-ball tribometer. CI-based TMP ester is a renewable lubricant that is nonedible, biodegradable, and nontoxic and has net zero greenhouse gases. The TMP ester was produced from CI oil, which has high lubricity properties such as higher density, higher viscosity at both 40°C and 100°C and higher viscosity index (VI). Experiments were conducted during 3,600 s with constant load of 40 kg and constant sliding speed of 1,200 rpm at temperatures of 50, 60, 70, 80, 90, and 100°C for all three types of lubricant. The results show that CI TMP ester had the lowest coefficient of friction (COF) as well as lower consumption of energy at all test temperatures, but the worn surface roughness average (R_a) and wear scar diameter were higher compared to paraffin mineral oil and commercial lubricant. Before 80°C, CI TMP ester actually has a higher flash temperature parameter (FTP) than paraffin mineral oil and as the temperature increases, the FTP of TMP ester decreases. The worn surfaces of the stationary balls were analyzed by scanning electron microscopy (SEM) and results show that CI TMP ester has the highest wear compared to paraffin mineral oil and lowest wear compared to commercial lubricant. However, CI TMP ester is environmentally desired, competitive to commercial lubricant, and its use should be encouraged.     

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.     

Photo-Fenton-like treatment of BPA: Effect of UV light source and water matrix on toxicity and transformation products

UV-A (near-UV), UV-C (short-UV) and visible-light assisted Fenton-like treatment of Bisphenol A (BPA) was investigated in pure water and raw freshwater samples spiked with BPA. Treatment performances were evaluated in terms of BPA degradation, dissolved organic carbon (DOC) removal and H₂O₂ consumption rates. Complete BPA degradation accompanied with significant DOC removal was achieved for all studied processes. Increasing the initial solution pH only exhibited a negative effect on treatment efficiencies when bicarbonate alkalinity was used for pH adjustment, whereas the raw freshwater matrix and irradiation type also influenced oxidation rates appreciably. Acute toxicity analysis employing Vibrio fischeri revealed that the inhibitory effect of BPA decreased significantly during the course of Photo-Fenton-like treatment. Several transformation products could be identified via HPLC and GC–MS analyses including hydroxylated phenolic compounds (hydroquinone; 2-methoxy, 1-4-benzenediol; 4-isopropenylphenol; 4′-hydroxy-acetophenone; 1-(4-cyclohexylphenyl) ethanone; 4-isopropylenecatechol; 4-4′-dihydroxybenzophenone; 4-ethyl,1,3-benzenediol), as well as the ring opening products hexanoic acid methyl ester, fumaric, succinic and oxalic acids. A reaction pathway featuring hydroxylation, dimerization and ring opening steps is proposed.     

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.     

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.