Synthesis and characterization of thiolated carboxymethyl chitosan-graft-cyclodextrin nanoparticles as a drug delivery vehicle for albendazole

A new strategy for the synthesis of thiolated carboxymethyl chitosan-g-cyclodextrin nanoparticles by an ionic-gelation method is presented. The synthetic approach was based on the utilization of 1,6-hexamethylene diisocyanate during cyclodextrin grafting onto carboxymethyl chitosan. The use of the 1,6-hexamethylene diisocyanate resulted in reactions between cyclodextrin and active sites at the C₆-position of chitosan, and preserved amino groups of chitosan for subsequent reactions with thioglycolic acid, as the thiolating agent, and tripolyphosphate, as the gelling counterion. Various methods such as scanning electron microscopy, rheology and in vitro release studies were employed to exhibit significant features of the nanoparticles for mucosal albendazole delivery applications. It was found that the thiolated carboxymethyl chitosan-g-cyclodextrin nanoparticles prepared using an aqueous solution containing 1 wt% of tripolyphosphate and having 115.65 (μmol/g polymer) of grafted thiol groups show both the highest mucoadhesive properties and the highest albendazole entrapment efficiency. The latter was confirmed theoretically by calculating the enthalpy of mixing of albendazole in the above thiolated chitosan polymer.     

A micromechanical approach for the prediction of the time-dependent failure of high temperature polymer matrix composites

In the present study, a novel micromechanical approach is introduced to study the time-dependent failure of unidirectional polymer matrix composites. The main advantage of the present micromechanical model lies in its ability to give closed-form solutions for the effective nonlinear response of unidirectional composites and to predict the material response to any combination of shear and normal loading. The creep failure criterion is expressed in terms of the creep failure functions of the viscoelastic matrix material. The micromechanical model is also used to calculate these creep failure functions from the knowledge of the creep behavior of the composite material in only transverse and shear loadings, thus eliminating the need for any further experimentation. The composite material used in this study is T300/934, which is suitable for service at high temperatures in aerospace applications. The use of micromechanics can give a more accurate insight into the failure mechanisms of the composite materials in particular at high temperatures where the general behavior of the polymer matrix composite is governed by matrix viscoelasticity and the time-dependent failure of the matrix is a localized phenomenon. The obtained creep failure stresses are found to be in reasonable agreement with the experimental data.     

Study of Structural and Magnetic Properties of Superparamagnetic Fe3O4–ZnO Core–Shell Nanoparticles

In this work, Fe₃O₄–ZnO core–shell nanoparticles have been successfully synthesized using a simple two-step co-precipitation method. In this regard, Fe₃O₄ (magnetite) and ZnO (zincite) nanoparticles (NPs) were synthesized separately. Then, the surface of the Fe₃O₄ NPs was modified with trisodium citrate in order to improve the attachment of ZnO NPs to the surface of Fe₃O₄ NPs. Afterwards, the modified magnetite NPs were coated with ZnO NPs. Moreover, the influence of the core to shell molar ratio on the structural and magnetic properties of the core–shell NPs has been investigated. The prepared nanoparticles have been characterized utilizing transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy and vibrating sample magnetometer (VSM). The results of XRD indicate that Fe₃O₄ NPs with inverse spinel phase were formed. The results of VSM imply that the Fe₃O₄–ZnO core–shell NPs are superparamagnetic. The saturation magnetization of prepared Fe₃O₄ NPs is 54.24 emu/g and it decreases intensively down to 29.88, 10.51 and 5.75 emu/g, after ZnO coating with various ratios of core to shell as 1:1, 1:10 and 1:20, respectively. This reduction is attributed to core–shell interface effects and shielding. TEM images and XRD results imply that ZnO-coated magnetite NPs are formed. According to the TEM images, the estimated average size for most of core–shell NPs is about 12 nm.     

Facile method for the preparation of the WS2 nanoparticles

A facile and simple synthetic route was proposed for the synthesis of WS2 nanoparticles. The as-prepared WS2 nanoparticles can be characterized with X-ray diffraction spectrum (XRD), Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). The average particle size of the product is about 85 nm that was calculated from XRD pattern by the Debye-Scherrer formula.     

Development of planning level transportation safety tools using Geographically Weighted Poisson Regression

A common technique used for the calibration of collision prediction models is the Generalized Linear Modeling (GLM) procedure with the assumption of Negative Binomial or Poisson error distribution. In this technique, fixed coefficients that represent the average relationship between the dependent variable and each explanatory variable are estimated. However, the stationary relationship assumed may hide some important spatial factors of the number of collisions at a particular traffic analysis zone. Consequently, the accuracy of such models for explaining the relationship between the dependent variable and the explanatory variables may be suspected since collision frequency is likely influenced by many spatially defined factors such as land use, demographic characteristics, and traffic volume patterns. The primary objective of this study is to investigate the spatial variations in the relationship between the number of zonal collisions and potential transportation planning predictors, using the Geographically Weighted Poisson Regression modeling technique. The secondary objective is to build on knowledge comparing the accuracy of Geographically Weighted Poisson Regression models to that of Generalized Linear Models. The results show that the Geographically Weighted Poisson Regression models are useful for capturing spatially dependent relationships and generally perform better than the conventional Generalized Linear Models.     

Performance properties of microcrystalline cellulose as a reinforcing agent in wood plastic composites

Polypropylene (PP)/microcrystalline cellulose (MCC)/wood flour composites were prepared containing polypropylene-graft-maleic anhydride (PP-g-MA) as compatibilizer. The mechanical, morphological and thermal properties were investigated. The weight ratio of the cellulosic materials to polymer matrix was 40:60 (w:w). The obtained results showed that tensile, flexural and impact strengths of the composites were significantly enhanced with addition of MCC, as compared with pure PP and composites without MCC. The effect of MCC on impact was minimal compared to the effects of PP-g-MA content. Scanning electron microscopy has shown that the composite, with compatibilizer, promotes better fiber–matrix interaction. In all cases, the degradation temperatures shifted to higher values after addition of PP-g-MA. The maximum improvement on the thermal stability of the composites was achieved when 5% PP-g-MA was used. However, the increase in MCC content substantially reduced the thermal stability. This work showed that MCC along with wood flour could be effectively used as reinforcing agent in thermoplastic matrix.     

New Approach to High-Pressure Nuclear Magnetic Resonance with Anvil Cells

A novel approach that uses radio-frequency microcoils in the high-pressure region of anvil cells with Nuclear Magnetic Resonance (NMR) experiments is described. High-sensitivity Al NMR data at 70 kbar for Al metal are presented for the first time. An expected decrease in the Al Knight shift at 70 kbar is observed, as well as an unexpected change in the local charge symmetry at the Al nucleus. The latter is not predicted by chemical structure analysis under high pressure.     

ConsilientSFL: using preferential voting system to generate combinatorial ranking metrics for spectrum-based fault localization

Spectrum-based fault localization (SFL) techniques have shown considerable effectiveness in localizing software faults. They leverage a ranking metric to automatically assign suspiciousness scores to certain entities in a given faulty program. However, for some programs, the current SFL ranking metrics lose effectiveness. In this paper, we introduce ConsilientSFL that is served to synthesize a new ranking metric for a given program, based on a customized combination of a set of given ranking metrics. ConsilientSFL can be significant since it demonstrates the usage of voting systems into a software engineering task. First, several mutated, faulty versions are generated for a program. Then, the mutated versions are executed with the test data. Next, the effectiveness of each existing ranking metric is computed for each mutated version. After that, for each mutated version, the computed existing metrics are ranked using a preferential voting system. Consequently, several top metrics are chosen based on their ranks across all mutated versions. Finally, the chosen ranking metrics are normalized and synthesized, yielding a new ranking metric. To evaluate ConsilientSFL, we have conducted experiments on 27 subject programs from Code4Bench and Siemens benchmarks. In the experiments, we found that ConsilientSFL outperformed every single ranking metric. In particular, for all programs on average, we have found performance measures recall, precision, f-measure, and percentage of code inspection, to be nearly 7, 9, 12, and 5 percentages larger than using single metrics, respectively. The impact of this work is twofold. First, it can mitigate the issue with the choice and usage of a proper ranking metric for the faulty program at hand. Second, it can help debuggers find more faults with less time and effort, yielding higher quality software.

     

An efficient parallel algorithm for the longest path problem in meshes

The longest path problem is the problem of finding a simple path with the maximum number of vertices in a given graph, and so far it has been solved polynomially only for a few classes of graphs. This problem generalizes the well-known Hamiltonian path problem, hence it is NP-hard in general graphs. In this paper, first we give a sequential linear-time algorithm for the longest path problem in meshes. Then based on this algorithm, we present a constant-time parallel algorithm for the problem, which can be run on every parallel machine.