Synthesis and characterization of poly(L-lactic acid-co-ethylene oxide-co-aspartic acid) and its interaction with cells

Multiblock terpolymer of poly(L-lactic acid)/ poly(ethylene oxide)/poly(L-aspartic acid), (PLLA/PEO/ PAsp) was synthesized by ring opening polymerization of β -benzyl L-aspartate N-carboxyanhydride, Asp(OBzl)-NCA with α-ω -hydroxy terminated triblock PLLA/PEO/PLLA copolymer. The resulting multiblock terpolymer was characterized by several techniques including Fourier transform infrared spectroscopy and differential scanning calorimetry.¹H nuclear magnetic resonance spectra indicated the molar ratio of PLLA/PEO/PAsp (OBzl) to be 86/10/4. Thermal gravimetric analysis and environmental scanning electron microscopy data showed that PLLA/PEO/PAsp had crystalline and brittle structure. In order to improve its mechanical and physical properties, the terpolymer was blended with high molecular weight poly(L-lactic-co-glycolic acid) copolymer, PLGA(85/15) (M_w: 95000 gmol^{− 1}) in 25/75 and 50/50 mole ratios. The hydrolytical degradation properties of these polymers were studied. Degradation experiments were performed during a 48-day period in pH:7.4 phosphate-buffered saline (PBS) at 37^∘C. The observed molecular weight losses were 91% and 67% for the 25/75 and 50/50 mixtures, respectively. In vitro attachment and growth of L929 mouse fibroblasts on these biopolymers were also investigated. Cell growth experiments indicated that the copolymer blend allowed the attachment and growth of cells.     

Toughening of unmodified polyvinylchloride through the addition of nanoparticulate calcium carbonate

PVC/CaCO₃ polymer nanocomposites of differing compositions were produced using a two-roll mill and compression molding. The morphology was observed using transmission electron microscopy, and the static and dynamic mechanical and fracture properties determined. The presence of nanometer-sized CaCO₃ particles led to a slight decrease in the tensile strength but improved the impact energy, the storage modulus and the fracture toughness. Fracture surface examination by scanning electron microscopy indicated that the enhanced fracture properties in the nanocomposites were caused by the assisted void formation at the particles. This hypothesis is supported by a microstructure-based finite element modeling based upon elastic-plastic deformation around a weakly bonded particle. Hence, this provides an explanation of both the uniaxial tensile behavior and enhanced toughness of the nanocomposites.     

Efficiency assessment of Turkish power plants using data envelopment analysis

The performance of electricity generation plants in Turkey are analyzed and compared. The data set contains inputs from 65 thermal, hydro and wind power plants, owned by private and public sectors. Data envelopment analysis (DEA) is used as the primary mathematical tool. Two efficiency indexes, reflecting operational and investment performance, are defined and pursued. Constant returns to scale, variable returns to scale and assurance region type DEA models are used in the analysis. Scale efficiency is also considered. Performance comparisons include public versus private sector plants, and natural gas versus coal versus oil fired plants. Also, relationships between efficiency scores and various input/output factors are investigated and some interesting trends are identified.     

An overview of Turkish lignite qualities by logistic analysis

As an indispensable energy source, lignite is almost exclusively used in power generation in Turkey. To assess the quality level of Turkish lignite, a multivariate statistical analysis was conducted. The relationship among the lignite quality parameters has been investigated using a response method that is the logistic regression method. The analysis determines the effect of multiple predictor variables such as moisture, ash and sulphur presented simultaneously to predict membership of the two calorific value categories. By this way, a reliable binary response regression structure was constructed considering all the lignite fields in Turkey. Both the experiments on identifying the influential measurements and the measure of goodness of fit indicated that the overall model has a big capability to exhibit the relationship among the parameters of the Turkish lignite.     

Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing

This paper presents a novel method for diagnosis of heart disease. The proposed method is based on a hybrid method that uses fuzzy weighted pre-processing and artificial immune recognition system (AIRS). Artificial immune recognition system has showed an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabetes, liver disorders classification. The robustness of the proposed method is examined using classification accuracy, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 96.30% and it is very promising compared to the previously reported classification techniques.     

The effect to diagnostic accuracy of decision tree classifier of fuzzy and k-NN based weighted pre-processing methods to diagnosis of erythemato-squamous diseases

This paper presents a novel method for differential diagnosis of erythemato-squamous disease. The proposed method is based on fuzzy weighted pre-processing, k-NN (nearest neighbor) based weighted pre-processing, and decision tree classifier. The proposed method consists of three parts. In the first part, we have used decision tree classifier to diagnosis erythemato-squamous disease. In the second part, first of all, fuzzy weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified using decision tree classifier. In the third part, k-NN based weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified via decision tree classifier. The employed decision tree classifier, fuzzy weighted pre-processing decision tree classifier, and k-NN based weighted pre-processing decision tree classifier have reached to 86.18, 97.57, and 99.00% classification accuracies using 20-fold cross validation, respectively.     

Testing the robustness of predictive models for chlorophyll generated from spaceborne imaging spectroscopy data for a mixedwood boreal forest canopy

The amount of chlorophyll in a leaf influences photosynthetic potential and can be an indicator of the overall condition of a plant, including its stress level and nutritional status. Hence, it is important to understand the spatial and temporal variation of chlorophyll concentration. Imaging spectroscopy (IS) has made it possible to estimate chlorophyll at leaf and canopy levels. Spaceborne imaging spectrometers offer the possibility of estimating chlorophyll concentration at larger spatial scales and at lower cost than from direct sampling. We undertook this study in a mixedwood boreal forest to test the robustness of predictive models generated using Hyperion data for predicting chlorophyll concentration of data sets from different locations collected in different years. Among the group of indices tested, the derivative chlorophyll index (DCI) (DCI = D₇₀₅/D₇₂₂) and the maximum derivative of the red-edge divided by the derivative of 703 nm (D_{max(680–750)})/D₇₀₃) emerged as the best predictors of chlorophyll concentration across space and through time. When the canopy level chlorophyll predictive models of DCI and D_{max(680–750)})/D₇₀₃ derived from Hyperion data were applied to other years’ remote-sensing data acquired by airborne and spaceborne sensors, DCI explained 71%, 63%, and 6% and D_{max(680–750)})/D₇₀₃ explained 61%, 54%, and 8% of the variation in chlorophyll in 2002, 2004, and 2008, respectively, with prediction errors ranging from 11.7% to 14.6%. Two-variable models generated using 2005 Hyperion data were not as robust for predicting chlorophyll concentration from other years. Two models were found to explain more than half of the variance in chlorophyll concentration for the 2004 data only. Single and two-variable models applied to 2008 chlorophyll data provided poor predictions. The presence of multiple species creates a gradient in the chlorophyll concentration, which makes it possible to predict chlorophyll concentration. The gradient also affects the performance of predictive models generated using data from a different year. However, differences in sensors may also affect model performance. Our results suggest that predictive models obtained from Hyperion data are robust in predicting chlorophyll concentration within the same site through time and also at different sites across sensors.