Synthesis and characterization of poly(N‐vinyl‐2‐pyrrolidone) grafted sodium alginate hydrogel beads for the controlled release of indomethacin

Graft copolymers of sodium alginate (NaAlg) with N‐vinyl‐2‐pyrrolidone were prepared using azobisisobutyronitrile as initiator. The graft copolymers (NaAlg‐g‐PVP) were characterized with Fourier transform infrared spectroscopy, elemental analysis, and differential scanning calorimetry. Polymeric hydrogel beads of NaAlg and NaAlg‐g‐PVP were prepared by crosslinking method using glutaraldehyde (GA) as a crosslinker in the hydrochloric acid catalyst (HCl) and these beads were used to deliver anti‐inflammatory drug, indomethacin (IM). Chemical stability of the IM after encapsulation into beads was confirmed by FTIR. Preparation conditions of the NaAlg‐g‐PVP beads were optimized by considering the percentage entrapment efficiency, particle size, swelling capacity and their release data. In vitro release studies were performed in simulated gastric fluid (pH 1.2) for the initial 2 h, followed by simulated intestinal fluid (pH 7.4) for 4 h. Effects of GA concentration, exposure time to GA, drug/polymer (d/p) ratio, and concentration of HCl on the release of IM were discussed. It was observed that IM release from the beads decreased with increasing GA concentration and exposure time. IM release also decreases with increasing d/p ratio and HCl concentration. The highest IM release was obtained to be 77% for beads crosslinked with 0.027M GA. Swelling experiments were also performed to compute molecular mass between crosslinks of the beads. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Prediction of suspended sediment concentration from water quality variables

This study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time- and cost-saving implications.     

Chemical,thermal, and mechanical properties and ultraviolet transmittance of novel nano-hydroxyapatite/polyethylene terephthalate milk bottles

Polyethylene terephthalate (PET)/nano-hydroxyapatite (nHAp) composite granules were obtained using twin-screw extruder. Preforms were prepared by injection molding and then PET/nHAp bottles were produced by blow molding. For PET bottles with nHAp, the migration amounts of carboxylic acid (COOH), acetaldehyde (AA), diethylene glycol (DEG), and isophthalic acid (IPA); glass transition temperature (T_g); melting temperature (T_m); and the maximum crystallization temperature (T_cry) were measured. The load-carrying capacity, burst strength, stress cracking, and regional material distribution tests were carried out on the bottles. X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, and ultraviolet transmittance analyses were conducted to explain the changes in mechanical, chemical, physical properties, and light transmission of bottles. It was found out that the COOH amount increased and the AA content decreased with increasing nHAp amount. On the other hand, no change was observed in the amounts of DEG and IPA. Although the mechanical properties such as load-carrying capacity and burst strength of the bottles have improved, it has been determined that the standard environmental stress crack resistance test procedure cannot be applied to such a composite. Experimental findings indicate that nHAp disrupts the chemical structure of PET and it isolates harmful chemicals such as AA by forming intermolecular bonds. Moreover, with the addition of up to 0.8% nHAp, PET bottles block the light transmission approximately 80% within 400–700 nm wave length zone. The study demonstrates that the PET/nHAp composite bottles can be used in the food industry, particularly in the packaging of milk and milk products which are vulnerable to light exposure.     

Fair referee assignments for professional football leagues

Assignment of referees to football games is an important problem faced in professional football leagues. Despite its importance, the problem has received limited academic attention. This paper presents a model and analysis of the problem for fair referee assignments, and develops a constructive heuristic and a local search procedure for its solution. Results from an extensive computational study show that the methods are effective in solving the problem in a second of computation time and yielding an excellent solution quality.     

Interpretation of MR images using self-organizing maps and knowledge-based expert systems

A new image segmentation system is presented to automatically segment and label brain magnetic resonance (MR) images to show normal and abnormal brain tissues using self-organizing maps (SOM) and knowledge-based expert systems. Elements of a feature vector are formed by image intensities, first-order features, texture features extracted from gray-level co-occurrence matrix and multiscale features. This feature vector is used as an input to the SOM. SOM is used to over segment images and a knowledge-based expert system is used to join and label the segments. Spatial distributions of segments extracted from the SOM are also considered as well as gray level properties. Segments are labeled as background, skull, white matter, gray matter, cerebrospinal fluid (CSF) and suspicious regions.     

Approximation by Stancu–Chlodowsky polynomials

In this paper, we investigate convergence and approximation properties of a Chlodowsky type generalization of Stancu polynomials (we called Stancu–Chlodowsky polynomials).The rates of convergence of this generalization are obtained by means of modulus of continuity and by using the $K$-functional of Peetre. We also present and prove theorems on weighted approximation and the order of approximation of continuous functions by these operators on all positive semi-axis.     

Natural frequencies of a flat viaduct road part simply supported on two ends

Viaduct roads have wide application in big cities with high traffic loads, in order to decrease traffic density and to connect subways to highways. Viaduct roads are constructed using steel structures instead of concrete ones in areas of earthquake risks. The low weight of steel structures however causes problems such as vibration and noise. There is increasing demand especially in populated areas to suppress vibration and noise on highway roads for reducing noise-related environmental pollution. In this study, bending vibrations of rectangular plate viaduct roads, which are supported by six fixed elements of rectangular cross-sectional elements are considered. Natural frequencies are obtained using the Rayleigh-Ritz technique, finite elements analysis, experimentally and neural networks (NN).     

Instance-Based Regression by Partitioning Feature Projections

A new instance-based learning method is presented for regression problems with high-dimensional data. As an instance-based approach, the conventional method, KNN, is very popular for classification. Although KNN performs well on classification tasks, it does not perform as well on regression problems. We have developed a new instance-based method, called Regression by Partitioning Feature Projections (RPFP) which is designed to meet the requirement for a lazy method that achieves high levels of accuracy on regression problems. RPFP gives better performance than well-known eager approaches found in machine learning and statistics such as MARS, rule-based regression, and regression tree induction systems. The most important property of RPFP is that it is a projection-based approach that can handle interactions. We show that it outperforms existing eager or lazy approaches on many domains when there are many missing values in the training data.