A reaction kinetic study of CO<Subscript>2</Subscript> gasification of petroleum coke,coals and mixture

Characteristics of Char-CO₂ gasification were compared in the temperature range of 1,100–1,400 °C using a thermogravimetric analyzer (TGA) for petroleum coke, coal chars and mixed fuels (Petroleum coke/coal ratios: 0, 0.25, 0.5, 0.75, 1). The results showed that reaction time decreased with increasing gasification temperature, BET surface area and alkali index of coal. Mixed fuels composed of petroleum coke/coal exhibited reduced activation energies. Modified volumetric reaction model and shrinking core model might be suitably matched with experimental data depending on coal type and petroleum coke/coal ratio. Rate equations were suggested by selecting gas-solid reaction rate models for each sample that could simulate CO₂ gasification behavior.     

Aptamer-modified magnetic nanoprobe for molecular MR imaging of VEGFR2 on angiogenic vasculature

Nucleic acid-based aptamers have been developed for the specific delivery of diagnostic nanoprobes. Here, we introduce a new class of smart imaging nanoprobe, which is based on hybridization of a magnetic nanocrystal with a specific aptamer for specific detection of the angiogenic vasculature of glioblastoma via magnetic resonance (MR) imaging. The magnetic nanocrystal imaging core was synthesized using the thermal decomposition method and enveloped by carboxyl polysorbate 80 for water solubilization and conjugation of the targeting moiety. Subsequently, the surface of the carboxylated magnetic nanocrystal was modified with amine-functionalized aptamers that specifically bind to the vascular growth factor receptor 2 (VEGFR2) that is overexpressed on angiogenic vessels. To assess the targeted imaging potential of the aptamer-conjugated magnetic nanocrystal for VEGFR2 markers, the magnetic properties and MR imaging sensitivity were investigated using the orthotopic glioblastoma mouse model. In in vivo tests, the aptamer-conjugated magnetic nanocrystal effectively targeted VEGFR2 and demonstrated excellent MR imaging sensitivity with no cytotoxicity.     

Effects of amplitude modulation on perception of wind turbine noise

Wind turbine noise is considered to be easily detectable and highly annoying at relatively lower sound levels than other noise sources. Many previous studies attributed this characteristic to amplitude modulation. However, it is unclear whether amplitude modulation is the main cause of these properties of wind turbine noise. Therefore, the aim of the current study is to identify the relationship between amplitude modulation and these two properties of wind turbine noise. For this investigation, two experiments were conducted. In the first experiment, 12 participants determined the detection thresholds of six target sounds in the presence of background noise. In the second experiment, 12 participants matched the loudness of modified sounds without amplitude modulation to that of target sounds with amplitude modulation. The results showed that the detection threshold was lowered as the modulation depth increased; additionally, sounds with amplitude modulation had higher subjective loudness than those without amplitude modulation.     

Ruthenium(II)‐ or Rhodium(III)‐Catalyzed Grignard‐Type Addition of Indolines and Indoles to Activated Carbonyl Compounds

The ruthenium(II)‐ or rhodium(III)‐catalyzed pyrimidinyl‐directed Grignard‐type C−H additions of N‐heterocycles with activated aldehydes and ketones are described. A cationic ruthenium catalyst and sodium acetate additive in dichloroethane as solvent were found to be optimal catalytic system for the construction of C‐7 alkylated indolines. In sharp contrast, a cationic rhodium complex allows the generation of C‐2 alkylated indoles and pyrroles as well as C‐1 alkylated carbazoles. The site‐selective C−H functionalization of these heterocyclic scaffolds could be an important asset towards the development of novel bioactive compounds.

     

Ensemble of multi-task deep convolutional neural networks using transfer learning for fruit freshness classification

Multimedia Tools and Applications - Automatic classification of fruit freshness plays an important role in the agriculture industry. In this work, we propose an ensemble model that combines the...     

Sulfonated poly(ether sulfone) electrolytes structured with mesonaphthobifluorene graphene moiety for PEMFC

Sulfonated poly(ether sulfone)s containing a mixture of cis and trans mesonaphthobifluorene moiety were synthesized, and their properties were characterized. The mesonaphthobifluorene graphene moiety contained 6 phenyl rings and was conjugated together to form planar sheets of sp²-bonded carbon. Poly(arylene ether sulfone)s containing a mixture of cis and trans tetraphenyl ethylene units were synthesized by polycondensation, and converted into graphene by intramolecular Friedel–Craft cyclization with Lewis acid (FeCl₃). The sulfonation was taken selectively on mesonaphthobifluorene units with concentrated sulfuric acid. The structural properties of the sulfonated polymers were investigated by ¹H NMR spectroscopy. The membranes were studied with regard to ion exchange capacity (IEC), water uptake, and proton conductivity.     

An augmented EDA with dynamic diversity control and local neighborhood search for coevolution of optimal negotiation strategies

In this paper, we present an estimation of distribution algorithm (EDA) augmented with enhanced dynamic diversity controlling and local improvement methods to solve competitive coevolution problems for agent-based automated negotiations. Since optimal negotiation strategies ensure that interacting agents negotiate optimally, finding such strategies—particularly, for the agents having incomplete information about their opponents—is an important and challenging issue to support agent-based automated negotiation systems. To address this issue, we consider the problem of finding optimal negotiation strategies for a bilateral negotiation between self-interested agents with incomplete information through an EDA-based coevolution mechanism. Due to the competitive nature of the agents, EDAs should be able to deal with competitive coevolution based on two asymmetric populations each consisting of self-interested agents. However, finding optimal negotiation solutions via coevolutionary learning using conventional EDAs is difficult because the EDAs suffer from premature convergence and their search capability deteriorates during coevolution. To solve these problems, even though we have previously devised the dynamic diversity controlling EDA (D²C-EDA), which is mainly characterized by a diversification and refinement (DR) procedure, D²C-EDA suffers from the population reinitialization problem that leads to a computational overhead. To reduce the computational overhead and to achieve further improvements in terms of solution accuracy, we have devised an improved D²C-EDA (ID²C-EDA) by adopting an enhanced DR procedure and a local neighborhood search (LNS) method. Favorable empirical results support the effectiveness of the proposed ID²C-EDA compared to conventional and the other proposed EDAs. Furthermore, ID²C-EDA finds solutions very close to the optimum.