Forecasting of entropy production due to buoyant convection using support vector machines (SVM) in a partially cooled square cross-sectional room

Forecasting of entropy generation of laminar natural convection in a partially cooled square cross-sectional room has been performed using support vector machines (SVM). The two-dimensional room was modeled as floor heating story with a window. Values of temperature and velocities were obtained by solving governing equations of natural convection with finite difference technique and using these values entropy generation was calculated with its definition. Forecasting of entropy generation due to fluid friction irreversibility (FFI) and heat transfer irreversibility (HTI) were made with known values for unknown parameters using SVM. Thus, calculation time was extremely reduced and values were obtained even for non-convergence cases. It was observed that the SVM was a strong method to predict the entropy generation without computational fluid dynamic analysis for all cases.     

Air and oxy-fuel combustion kinetics of low rank lignites

The air and oxy-fuel combustion processes of two low-grade lignite coals were investigated by thermogravimetric analysis (TGA) method. Coals were provided from two different coal mines in the Aegean region of Turkey. Oxy-fuel combustion experiments were carried out with three different gas mixtures of 21% O₂–79% CO₂; 40% O₂–60% CO₂ and 50% O₂–50% CO₂ at 950 °C and heating rates of 10 °C/min, 20 °C/min and 40 °C/min. The kinetics of the oxy-fuel combustion of coals were studied by using four different methods namely, Coats-Redfern (model-fitting method), Friedman (FR), Flynn–Wall–Ozawa's (FWO) and Kissinger–Akahira–Sunose's (KAS) methods. The apparent activation energies of combustion process calculated by FWO method are slightly but systematically higher than that calculated by the KAS and FR methods for the oxy-fuel atmospheres. Combustion behavior of both coals in the oxy-fuel combustion environment could vary significantly, likely due to their characteristics such ash and volatile matter contents.     

Early prostate cancer diagnosis by using artificial neural networks and support vector machines

The aim of this study is to design a classifier based expert system for early diagnosis of the organ in constraint phase to reach informed decision making without biopsy by using some selected features. The other purpose is to investigate a relationship between BMI (body mass index), smoking factor, and prostate cancer. The data used in this study were collected from 300 men (100: prostate adenocarcinoma, 200: chronic prostatism or benign prostatic hyperplasia). Weight, height, BMI, PSA (prostate specific antigen), Free PSA, age, prostate volume, density, smoking, systolic, diastolic, pulse, and Gleason score features were used and independent sample t-test was applied for feature selection. In order to classify related data, we have used following classifiers; scaled conjugate gradient (SCG), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and Levenberg–Marquardt (LM) training algorithms of artificial neural networks (ANN) and linear, polynomial, and radial based kernel functions of support vector machine (SVM). It was determined that smoking is a factor increases the prostate cancer risk whereas BMI is not affected the prostate cancer. Since PSA, volume, density, and smoking features were to be statistically significant, they were chosen for classification. The proposed system was designed with polynomial based kernel function, which had the best performance (accuracy: 79%). In Turkish Family Health System, family physician to whom patients are applied firstly, would contribute to extract the risk map of illness and direct patients to correct treatments by using expert system such proposed.     

Fatty acid hydratase for value-added biotransformation: A review

The synthesis of hydroxy fatty acids (HFAs) from renewable oil feedstock by addition of water onto C=C bonds has attracted great attention in recent years. Given that selective asymmetric hydration of non-activated C=C bonds has been proven difficult to achieve with chemical catalysts, enzymatic catalysis by fatty acid hydratases (FAHs) presents an attractive alternative approach to produce value-added HFAs with high regio-, enantioand stereospecificity, as well as excellent atom economy. Even though FAHs have just been investigated as a potential biocatalyst for a decade, remarkable information about FAHs in different aspects is available; however, a comprehensive review has not been archived. Herein, we summarize the research progresses on biochemical characterization, structural and mechanistic determination, enzyme engineering, as well as biotechnological application of FAHs. The current challenges and opportunities for an efficient utilization of FAHs in organic synthesis and industrial applications are critically discussed.     

K-shell absorption jump factors and jump ratios in elements between Tm (Z = 69) and Os (Z = 76) derived from new mass attenuation coefficient measurements

The K-shell absorption jump factors and jump ratios were derived from new mass attenuation coefficients measured using an energy dispersive X-ray fluorescence (EDXRF) spectrometer for Tm, Yb elements being Tm₂O₃, Yb₂O₃ compounds and pure Lu, Hf, Ta, W, Re and Os. The measurements, in the region 56-77 keV, were done in a transmission geometry utilizing the K_α1, K_α2, K_β1 and K_β2 X- rays from different secondary source targets (Yb, Ta, Os, W, Re and Ir, etc.) excited by the 123.6 keV γ-photons from an ⁵⁷Co annular source and detected by an Ultra-LEGe solid state detector with a resolution of 150 eV at 5.9 keV. Experimental results have been compared with theoretically calculated values. The measured values of Tm, Yb, Lu, Hf, Ta, W, Re and Os are reported here for the first time.     

Estimation of thermal and flow fields due to natural convection using support vector machines (SVM) in a porous cavity with discrete heat sources

Support vector machines (SVM), a soft programming technique, has been used to estimate the temperature distribution and flow fields in a square porous enclosure heated discretely by three isothermal heaters from the left vertical wall. Right vertical wall of the cavity was isothermal but it has colder temperature than the heaters while remaining walls were adiabatic. A database was prepared by solving the governing equations which were written using Darcy flow model. Using finite difference method to discretize the equation, a computational fluid dynamics (CFD) code was written. A correlation was developed between Nusselt and Rayleigh numbers. Using obtained database, further values of temperature and velocities were estimated by SVM technique at different Rayleigh numbers and locations of heater. It was observed that SVM was a useful technique on estimation of streamlines and isotherms. Thus, SVM reduces the computational time and helps to solve some cases when CFD fails to solve due to numerical instability.     

Electrospun nylon 6,6 nanofibers functionalized with cyclodextrins for removal of toluene vapor

Functional nylon 6,6 nanofibers incorporating cyclodextrins (CD) were developed via electrospinning. Enhanced thermal stability of the nylon 6,6/CD nanofibers was observed due to interaction between CD and nylon 6,6. X‐ray photoelectron spectroscopy and attenuated total reflectance Fourier transform infrared spectroscopy studies indicated the existence of some CD molecules on the surface of the nanofibers. Electrospun nylon 6,6 nanofibers without having CD were ineffective for entrapment of toluene vapor from the environment, whereas nylon 6,6/CD nanofibrous membranes can effectively entrap toluene vapor from the surrounding by taking advantage of the high surface‐volume ratio of nanofibers with the added advantage of inclusion complexation capability of CD presenting on the nanofiber surface. The modeling studies for formation of inclusion complex between CD and toluene were also performed by using ab initio techniques. Our results suggest that nylon 6,6/CD nanofibrous membranes may have potential to be used as air filters for the removal of organic vapor waste from surroundings. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41941.     

Hierarchical quantization indexing for wavelet and wavelet packet image coding

In this paper, we introduce the quantization index hierarchy, which is used for efficient coding of quantized wavelet and wavelet packet coefficients. A hierarchical classification map is defined in each wavelet subband, which describes the quantized data through a series of index classes. Going from bottom to the top of the tree, neighboring coefficients are combined to form classes that represent some statistics of the quantization indices of these coefficients. Higher levels of the tree are constructed iteratively by repeating this class assignment to partition the coefficients into larger subsets. The class assignments are optimized using a rate-distortion cost analysis. The optimized tree is coded hierarchically from top to bottom by coding the class membership information at each level of the tree. Context-adaptive arithmetic coding is used to improve coding efficiency. The developed algorithm produces PSNR results that are better than the state-of-art wavelet-based and wavelet packet-based coders in literature.