Synthesis of nanosized SiC by low-temperature magnesiothermal reduction of nanocomposites of functionalized carbon nanotubes with MCM-48

Silicon carbide synthesis by a magnesiothermal method was investigated using MCM-48 as the silica source mechanically mixed with carbon nanotubes (CNTs) as the carbon source, and nanocomposites of MCM-48/functionalized CNTs (CNTF). SiC syntheses were carried out with different molar ratios of MCM-48, carbon and magnesium at 700?°C in argon. The MCM-48 and carbon nanotube starting materials and the SiC products were characterized by BET, XRD, FESEM, EDX and TEM. The effect of the carbon content and the type of CNTs (either functionalized or unfunctionalized) on the SiC synthesis was studied. The results show that an improved yield of SiC is obtained when the carbon nanotubes are functionalized, producing a better contact with the MCM-48. This improved contact between the reactants ensures a good degree of reaction in a stoichiometric mixture of silicon and carbon, with no improvement in product formation being achieved by the use of additional carbon. These findings suggest that the degree of contact between reactants is an important factor in the magnesiothermal synthesis of SiC. The SiC products from magnesiothermal synthesis of the functionalized nanocomposite precursors were shown by TEM and FESEM to have unusual nanofiber morphologies mimicking the morphology of the CNTF nanotubes.     

Role of Ribes khorassanicum in the biosynthesis of AgNPs and their antibacterial properties

Silver nanoparticles (AgNPs) have been biosynthesised through the extracts of Ribes khorassanicum fruits, which served as the reducing agents and capping agents. Biosynthesised AgNPs have been found to be ultraviolet–visible (UV–vis) absorption spectra since they have displayed one surface plasmon resonance peak at 438 nm, attesting the formation of spherical NPs. These particles have been characterised by UV–vis, field‐emission scanning electron microscopy, energy‐dispersive X‐ray spectroscopy, X‐ray diffraction, Fourier transform infrared spectroscopy, and transmission electron microscopy analysis. The formation of AgNPs at 1.0 mM concentration of AgNO₃ has resulted in NPs that contained mean diameters in a range of 20–40 nm. The green‐synthesised AgNPs have demonstrated high antibacterial effect against pathogenic bacteria (i.e. Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa). Biosynthesising metal NPs through plant extracts can serve as the facile and eco‐friendly alternative for chemical and/or physical methods that are utilised for large‐scale nanometal fabrication in various medical and industrial applications.Inspec keywords: X‐ray diffraction, X‐ray chemical analysis, nanofabrication, surface plasmon resonance, nanoparticles, antibacterial activity, microorganisms, scanning electron microscopy, silver, nanomedicine, visible spectra, ultraviolet spectra, transmission electron microscopy, Fourier transform infrared spectra, field emission scanning electron microscopy, biomedical materialsOther keywords: antibacterial properties, silver nanoparticles, reducing agents, capping agents, surface plasmon resonance peak, spherical NPs, field‐emission scanning electron microscopy, energy‐dispersive X‐ray spectroscopy, X‐ray diffraction, transmission electron microscopy analysis, plant extracts, ultraviolet‐visible absorption spectra, Fourier transform infrared spectroscopy, antibacterial effect, Ribes khorassanicum fruits, Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, surface plasmon resonance, AgNO₃ , Ag     

Course timetabling using evolutionary operators

Timetabling is the problem of scheduling a set of events while satisfying various constraints. In this paper, we develop and study the performance of an evolutionary algorithm, designed to solve a specific variant of the timetabling problem. Our aim here is twofold: to develop a competitive algorithm, but more importantly, to investigate the applicability of evolutionary operators to timetabling. To this end, the introduced algorithm is tested using a benchmark set. Comparison with other algorithms shows that it achieves better results in some, but not all instances, signifying strong and weak points. To further the study, more comprehensive tests are performed in connection with another evolutionary algorithm that uses strictly group-based operators. Our analysis of the empirical results leads us to question single-level selection, proposing, in its place, a multi-level alternative.     

Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling

The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young’s modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R ²). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs.     

CMRules: Mining sequential rules common to several sequences

Sequential rule mining is an important data mining task used in a wide range of applications. However, current algorithms for discovering sequential rules common to several sequences use very restrictive definitions of sequential rules, which make them unable to recognize that similar rules can describe a same phenomenon. This can have many undesirable effects such as (1) similar rules that are rated differently, (2) rules that are not found because they are considered uninteresting when taken individually, (3) and rules that are too specific, which makes them less likely to be used for making predictions. In this paper, we address these problems by proposing a more general form of sequential rules such that items in the antecedent and in the consequent of each rule are unordered. We propose an algorithm named CMRules for mining this form of rules. The algorithm proceeds by first finding association rules to prune the search space for items that occur jointly in many sequences. Then it eliminates association rules that do not meet the minimum confidence and support thresholds according to the sequential ordering. We evaluate the performance of CMRules in three different ways. First, we provide an analysis of its time complexity. Second, we compare its performance (in terms of execution time, memory usage and scalability) with an adaptation of an algorithm from the literature that we name CMDeo. For this comparison, we use three real-life public datasets, which have different characteristics and represent three kinds of data. In many cases, results show that CMRules is faster and has a better scalability for low support thresholds than CMDeo. Lastly, we report a successful application of the algorithm in a tutoring agent.     

An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity

The pile bearing capacity is considered as the most essential factor in designing deep foundations. Direct determination of this parameter in site is costly and difficult. Hence, this study presents a new technique of intelligence system based on the adaptive neuro-fuzzy inference system (ANFIS)-group method of data handling (GMDH) optimized by the imperialism competitive algorithm (ICA), ANFIS-GMDH-ICA for forecasting pile bearing capacity. In this advanced structure, the ICA role is to optimize the membership functions obtained by ANFIS-GMDH technique for receiving a higher accuracy level and lower error. To develop this model, the results of 257 high strain dynamic load tests (performed by authors) were considered and used in the analysis. For comparison purposes, ANFIS and GMDH models were selected and built for pile bearing capacity estimation. In terms of model accuracy, the obtained results showed that the newly developed model (i.e., ANFIS-GMDH-ICA) receives more accurate predicted values of pile bearing capacity compared to those obtained by ANFIS and GMDH predictive models. The proposed ANFIS-GMDH-ICA can be utilized as an advanced, applicable and powerful technique in issues related to foundation engineering and its design.

     

Antimicrobial Ionic Liquid-Based Materials for Biomedical Applications

Excessive and unwarranted administration of antibiotics has invigorated the evolution of multidrug-resistant microbes. There is, therefore, an urgent need for advanced active compounds. Ionic liquids with short-lived ion-pair structures are highly tunable and have diverse applications. Apart from their unique physicochemical features, the newly discovered biological activities of ionic liquids have fascinated biochemists, microbiologists, and medical scientists. In particular, their antimicrobial properties have opened new vistas in overcoming the current challenges associated with combating antibiotic-resistant pathogens. Discussions regarding ionic liquid derivatives in monomeric and polymeric forms with antimicrobial activities are presented here. The antimicrobial mechanism of ionic liquids and parameters that affect their antimicrobial activities, such as chain length, cation/anion type, cation density, and polymerization, are considered. The potential applications of ionic liquids in the biomedical arena, including regenerative medicine, biosensing, and drug/biomolecule delivery, are presented to stimulate the scientific community to further improve the antimicrobial efficacy of ionic liquids.     

Linear estimator for fractional systems

We address the issue of state estimation of nonlinear incommensurate fractional-order systems via linear observer in this paper. The basic idea is proposed under a synchronization framework which makes the response system a linear observer for the state of the drive system. By developing this approach, a linear time-invariant synchronization error system is obtained, and stability analysis is relied on the theory of linear incommensurate fractional-order systems. The suggested tool proves to be effective and systematic in achieving global synchronization. Simulation results verify and illustrate the effectiveness of the proposed method on some new fractional-order hyperchaotic systems.     

Injectable and Crosslinkable PLGA‐Based Microribbons as 3D Macroporous Stem Cell Niche

Poly(lactide‐co‐glycolide) (PLGA) has been widely used as a tissue engineering scaffold. However, conventional PLGA scaffolds are not injectable, and do not support direct cell encapsulation, leading to poor cell distribution in 3D. Here, a method for fabricating injectable and intercrosslinkable PLGA microribbon‐based macroporous scaffolds as 3D stem cell niche is reported. PLGA is first fabricated into microribbon‐shape building blocks with tunable width using microcontact printing, then coated with fibrinogen to enhance solubility and injectability using aqueous solution. Upon mixing with thrombin, firbornogen‐coated PLGA microribbons can intercrosslink into 3D scaffolds. When subject to cyclic compression, PLGA microribbon scaffolds exhibit great shock‐absorbing capacity and return to their original shape, while conventional PLGA scaffolds exhibit permanent deformation after one cycle. Using human mesenchymal stem cells (hMSCs) as a model cell type, it is demonstrated that PLGA μRB scaffolds support homogeneous cell encapsulation, and robust cell spreading and proliferation in 3D. After 28 days of culture in osteogenic medium, hMSC‐seeded PLGA μRB scaffolds exhibit an increase in compressive modulus and robust bone formation as shown by staining of alkaline phosphatase, mineralization, and collagen. Together, the results validate PLGA μRBs as a promising injectable, macroporous, non‐hydrogel‐based scaffold for cell delivery and tissue regeneration applications.     

SmartSenior: Automatic Content Personalization Through Semi-supervised Learning

Wireless Personal Communications - The adaption of user interface (UI) promises to greatly enhance user experience (UX). This is more evident when we focus on elderly people. However, to date there...