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     

Accurate Receptor-Ligand Binding Free Energies from Fast QM Conformational Chemical Space Sampling

Small molecule receptor-binding is dominated by weak, non-covalent interactions such as van-der-Waals hydrogen bonding or electrostatics. Calculating these non-covalent ligand-receptor interactions is a challenge to computational means in terms of accuracy and efficacy since the ligand may bind in a number of thermally accessible conformations. The conformational rotamer ensemble sampling tool (CREST) uses an iterative scheme to efficiently sample the conformational space and calculates energies using the semi-empirical ‘Geometry, Frequency, Noncovalent, eXtended Tight Binding’ (GFN2-xTB) method. This combined approach is applied to blind predictions of the modes and free energies of binding for a set of 10 drug molecule ligands to the cucurbit[n]urils CB[8] receptor from the recent ‘Statistical Assessment of the Modeling of Proteins and Ligands’ (SAMPL) challenge including morphine, hydromorphine, cocaine, fentanyl, and ketamine. For each system, the conformational space was sufficiently sampled for the free ligand and the ligand-receptor complexes using the quantum chemical Hamiltonian. A multitude of structures makes up the final conformer-rotamer ensemble, for which then free energies of binding are calculated. For those large and complex molecules, the results are in good agreement with experimental values with a mean error of 3 kcal/mol. The GFN2-xTB energies of binding are validated by advanced density functional theory calculations and found to be in good agreement. The efficacy of the automated QM sampling workflow allows the extension towards other complex molecular interaction scenarios.     

An investigation of some Schiff bases as corrosion inhibitors for austenitic chromium–nickel steel in H2SO4

The corrosion inhibition of austenitic chromium–nickel steel by two Schiff bases, N-(1-toluidine)salicylaldimine and N-(2-hydroxyphenyl)salicylaldimine, was investigated in sulphuric acid medium. The effect of concentration and temperature on inhibition properties was determined. It was found that when the concentrations of inhibitor were increased the inhibition efficiencies () and surface coverage () increased. Some thermodynamic parameters such as free energy of adsorption, G _ads, and enthalpy, H, were determined for the Schiff bases. Experimental results agree with the Temkin isotherm for N-(1-toluidine)salicylaldimine, but the Langmuir isotherm is more appropriate for N-(2-hydroxyphenyl)salicylaldimine.     

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.     

The advantages of using impact resonance test in dynamic modulus master curve construction through the abbreviated test protocol

Asphalt concrete behavior is heavily dependent on temperature and loading rate. Hence, the material is typically tested at a range of temperatures and loading frequencies to capture its properties. Results are then used to develop a master curve exhibiting material behavior at the full spectrum of loading frequencies. An abbreviated testing protocol, under AASHTO PP 61-13, proposes a practical approach for development of this master curve. In this practice, the low temperature asymptote of the master curve is dominated by the limiting maximum modulus estimated through the Hirsch model. In this study, the dynamic modulus (DM) testing coupled with impact resonance (IR) test was used to evaluate the effect of this limiting maximum modulus on construction of the asphalt concrete master curve. Three different asphalt mixtures prepared with the same gradation and binder content, but different grades of stiffness were tested. The DM testing was performed at multiple temperatures and loading frequencies. The IR tests were conducted on the same specimens at the same temperatures. Two sigmoid functions (MEPDG and Richards models), and three shift factors (Arrhenius, Williams–Landel–Ferry, and polynomial) were utilized in the analysis. Richards sigmoid function coupled with polynomial shift factor provided the best fitting accuracy to the measured data. It was observed that the limiting maximum modulus obtained from experimental data was underpredicted by that obtained from the Hirsch model. The results indicated potential benefits of the IR test as a complementary testing tool to the abbreviated DM testing protocol to reliably characterize asphalt concrete.     

Estimating Construction Material Indices with ARIMA and Optimized NARNETs

Construction Industry operates relying on various key economic indicators. One of these indicators is material prices. On the other hand, cost is a key concern in all operations of the construction industry. In the uncertain conditions, reliable cost forecasts become an important source of information. Material cost is one of the key components of the overall cost of construction. In addition, cost overrun is a common problem in the construction industry, where nine out of ten construction projects face cost overrun. In order to carry out a successful cost management strategy and prevent cost overruns, it is very important to find reliable methods for the estimation of construction material prices. Material prices have a time dependent nature. In order to increase the foreseeability of the costs of construction materials, this study focuses on estimation of construction material indices through time series analysis. Two different types of analysis are implemented for estimation of the future values of construction material indices. The first method implemented was Autoregressive Integrated Moving Average (ARIMA), which is known to be successful in estimation of time series having a linear nature. The second method implemented was Non-Linear Autoregressive Neural Network (NARNET) which is known to be successful in modeling and estimating of series with non-linear components. The results have shown that depending on the nature of the series, both these methods can successfully and accurately estimate the future values of the indices. In addition, we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.     

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.