Multiple Regression Analysis of Catalytic Dehydrogenation of Isopropanol in a Chemical Heat Pump System

Chemical heat pump is a clean technology developed to upgrade the low‐level thermal energy to upper levels and to store energy without losses caused by temperature differences. Multiple regression analysis of catalytic dehydrogenation of isopropanol was performed. The endothermic dehydrogenation of isopropanol was carried out under continuous boiling and refluxing conditions in order to study the enhancement effects of the presence of an alkaline compound and different types of catalysts at various concentrations in the reaction medium on the evolution rate of hydrogen. The factorial experimental design method was applied to understand better the coupled influences of both catalyst and alkaline additive concentrations to discuss and evaluate statistically the results for different catalysts and to develop the related models.     

Ultrasonic properties of polystyrene-based composites

In this study, first the pure polystyrenes (PS) with different molecular weights (350 × 10³ and 500 × 10³) have been modified by the chemical modification with succinic anhydride (SA), maleic anhydride (MA), and phthalic anhydride (PhA) and then the polystyrene based composites (CPS) prepared by addition of modified polystyrenes (MPS) into pure PS (with the molecular weight of 230 × 10³) in weight % ratios of 90 : 10, 80 : 20, and 70 : 30. Ultrasonic measurements were performed on PS/MPS blends of different weight percent of MPSs by use of pulse echo method with 5-MHz frequency at room temperature. Elastic properties namely; longitudinal modulus (L), Young's modulus (E), bulk modulus (K) and shear modulus (G), Poisson's ratio (μ), and acoustic impedance (Z) were calculated from the ultrasonic velocities values measured and densities values obtained experimentally. Atomic force microscopy (AFM) has been used for determining the microstructure of composites. The variations of these parameters with increasing MPSs weight percentage content in PS/MPS from 10 to 30 have been discussed. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Does laser etching have an effect on application mode of a universal adhesive?—A microleakage and scanning electron microscopy evaluation

This study aimed to evaluate the microleakage of a universal adhesive's different application modes incorporated with Er,Cr:YSGG laser on Class V resin composite restorations. Sound human molar teeth (n = 30) were used for microleakage evaluations. Specimens with 60 standardized Class V cavities were divided into five groups according to the adhesive modes of universal adhesive, Adhese Universal (n = 12). Group 1‐etch‐and‐rinse mode with phosphoric acid; Group 2‐etch‐and‐rinse mode with Er,Cr:YSGG laser; Group 3‐selective‐etch mode with phosphoric acid; Group 4‐selective‐etch mode with Er,Cr:YSGG laser; Group 5‐self‐etch. After restorations were performed with a resin composite, Tetric N‐Ceram, the specimens were polished and subjected to thermocycling (10,000X). Following immersion in 0.5% basic fuschin for a day, the teeth were sectioned and the degree of microleakage was determined along the tooth‐resin composite interface using a light microscopy(40X). Five specimens from each group were examined by scanning electron microscopy. The Kruskal–Wallis, Siegel Castello, and Wilcoxon tests were used for statistical analyses (α = .05). At the enamel margins, significant differences were obtained among the groups (p < .05). Significantly higher microleakage scores were detected in Group 5 in comparison with Groups 1, 2, and 3. There were no significant differences between different adhesive strategies at the dentin margins (p > .05). While analyzing enamel and dentin microleakage scores, no statistically significant differences were observed in Groups 4 and 5 (p > .05). The laser application time and the adhesive modes of universal adhesives could affect the microleakage at the enamel margins. Different adhesive modes of universal adhesives combined with laser etching had no influence on the microleakage scores of dentin margins.     

Glucose recognition capabilities of hydroxyethyl methacrylate‐based hydrogels containing poly(ethylene glycol) chains

A configurational biomimetic imprinting technique was used to prepare recognition sites for glucose in copolymers of 2‐hydroxyethyl methacrylate (HEMA) and methacrylic acid (MAA) prepared with crosslinking agents containing poly(ethylene glycol) (PEG). We report on the structure, diffusive, and recognition characteristics of these gels, the effect of the type and ratio of crosslinking agent, as well as the template/comonomer ratios on glucose binding ability. The highest equilibrium glucose binding was found as 2.67 mg/g dry polymer when PEG monomethacrylate (PEGMMA) was used in combination with tetra ethylene glycol dimethacrylate (TEGDMA) (50%) as a crosslinking agent. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 103: 432–441, 2007     

Estimation of Monthly Mean Reference Evapotranspiration in Turkey

Monthly mean reference evapotranspiration (ET ₀ ) is estimated using adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN) models. Various combinations of long-term average monthly climatic data of wind speed, air temperature, relative humidity, and solar radiation, recorded at stations in Turkey, are used as inputs to the ANFIS and ANN models so as to calculate ET ₀ given by the FAO-56 PM (Penman-Monteith) equation. First, a comparison is made among the estimates provided by the ANFIS and ANN models and those by the empirical methods of Hargreaves and Ritchie. Next, the empirical models are calibrated using the ET ₀ values given by FAO-56 PM, and the estimates by the ANFIS and ANN techniques are compared with those of the calibrated models. Mean square error, mean absolute error, and determination coefficient statistics are used as comparison criteria for evaluation of performances of all the models considered. Based on these evaluations, it is found that the ANFIS and ANN schemes can be employed successfully in modeling the monthly mean ET ₀ , because both approaches yield better estimates than the classical methods, and yet ANFIS being slightly more successful than ANN.     

Rapid and Digital Detection of Inflammatory Biomarkers Enabled by a Novel Portable Nanoplasmonic Imager

New point‐of‐care diagnostic devices are urgently needed for rapid and accurate diagnosis, particularly in the management of life‐threatening infections and sepsis, where immediate treatment is key. Sepsis is a critical condition caused by systemic response to infection, with chances of survival drastically decreasing every hour. A novel portable biosensor based on nanoparticle‐enhanced digital plasmonic imaging is reported for rapid and sensitive detection of two sepsis‐related inflammatory biomarkers, procalcitonin (PCT) and C‐reactive protein (CRP) directly from blood serum. The device achieves outstanding limit of detection of 21.3 pg mL^?1 for PCT and 36 pg mL^?1 for CRP, and dynamic range of at least three orders of magnitude. The portable device is deployed at Vall d'Hebron University Hospital in Spain and tested with a wide range of patient samples with sepsis, noninfectious systemic inflammatory response syndrome (SIRS), and healthy subjects. The results are validated against ultimate clinical diagnosis and currently used immunoassays, and show that the device provides accurate and robust performance equivalent to gold‐standard laboratory tests. Importantly, the plasmonic imager can enable identification of PCT levels typical of sepsis and SIRS patients in less than 15 min. The compact and low‐cost device is a promising solution for assisting rapid and accurate on‐site sepsis diagnosis.     

Assessing facial beauty through proportion analysis by image processing and supervised learning

Perception of universal facial beauty has long been debated amongst psychologists and anthropologists. In this paper, we perform experiments to evaluate the extent of universal beauty by surveying a number of diverse human referees to grade a collection of female facial images. Results obtained show that there exists a strong central tendency in the human grades, thus exhibiting agreement on beauty assessment. We then trained an automated classifier using the average human grades as the ground truth and used it to classify an independent test set of facial images. The high accuracy achieved proves that this classifier can be used as a general, automated tool for objective classification of female facial beauty. Potential applications exist in the entertainment industry, cosmetic industry, virtual media, and plastic surgery.     

A steady-state genetic algorithm for multi-product supply chain network design

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management (SCM). The problem is often an important and strategic operations management problem in SCM. The design task involves the choice of facilities (plants and distribution centers (DCs)) to be opened and the distribution network design to satisfy the customer demand with minimum cost. This paper presents a solution procedure based on steady-state genetic algorithms (ssGA) with a new encoding structure for the design of a single-source, multi-product, multi-stage SCN. The effectiveness of the ssGA has been investigated by comparing its results with those obtained by CPLEX, Lagrangean heuristic, hyrid GA and simulated annealing on a set of SCN design problems with different sizes.     

Exploring the types and reasons of Internet-triggered academic dishonesty among Turkish undergraduate students: Development of Internet-Triggered Academic Dishonesty Scale (ITADS)

Internet contributes to the development of science and facilitates scientific demeanors while it also serves as a ground for academic misdemeanors. Recent studies indicate that Internet facilitates and spreads academic dishonesty. The purpose of the current study is to investigate the extent of involvement of Turkish university students in academic dishonesty practices facilitated through Internet (i.e. e-dishonesty) and to question the conditions which lead to e-dishonesty. Three hundred and forty nine education faculty students from the most populated state university in Turkey were administered two Likert-Scale questionnaires developed by the researchers. After the reliability and validity conditions were met, two exploratory factor analyses were conducted. The first one revealed the factors constituting common types of e-dishonesty among undergraduate students which were fraudulence, plagiarism, falsification, delinquency, and unauthorized help. The second factor analysis exhibited individual and contextual factors triggering e-dishonesty which were named as individual factors, institutional policies and peer pressure. Results of both analyses are discussed and suggestions for further research are provided.     

hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems

The aim of this work is to propose a hybrid heuristic approach (called hGA) based on genetic algorithm (GA) and integer-programming formulation (IPF) to solve high dimensional classification problems in linguistic fuzzy rule-based classification systems. In this algorithm, each chromosome represents a rule for specified class, GA is used for producing several rules for each class, and finally IPF is used for selection of rules from a pool of rules, which are obtained by GA. The proposed algorithm is experimentally evaluated by the use of non-parametric statistical tests on seventeen classification benchmark data sets. Results of the comparative study show that hGA is able to discover accurate and concise classification rules.