Object-based hyperspectral image classification using a new latent block model based on hidden Markov random fields

Pattern Analysis and Applications - Efficient high-dimensional analyses of hyperspectral datasets and their utilization within classification algorithms is a popular topic in the field of data...     

Design of bandpass–bandpass diplexers using rectangular-, T-, and L-shaped resonators for hybrid power amplifier and 5G applications

Analog Integrated Circuits and Signal Processing - In this paper, two bandpass–bandpass diplexers, based on L-, T- and rectangular-shaped resonators are designed and analyzed, which are used...     

Removal of iron from milk of lime to produce pure precipitated calcium carbonate

ABSTRACT

Milk of lime (MOL) suspension is widely utilized in many industries; however, in some of the applications, such as the production of precipitated calcium carbonate (PCC), the suspension must be of high purity and quality. In other words, the amounts of iron and magnesium should be so little so that the final PCC product would be of high quality. The effects of initial temperature, quicklime particle size, amount of initial solid present, agitation rate, and time on the recovery of hydrated lime and iron/magnesium reduction efficiency were investigated through a central composite design (CCD) of experiments. The iron reduction efficiency and the weight recovery of hydrated lime were obtained as 91% and 98.5%, respectively. The reduction of magnesium was not significant in this stage. Moreover, approximately the whole existing SiO₂ was eliminated.     

F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems

The Journal of Supercomputing - Internet of Things (IoT) is an emerging paradigm that consists of numerous connected and interrelated devices with embedded sensors, exchanging data with each other...     

Ionization coefficient of monolayer graphene nanoribbon

A semi-analytical model for impact ionization coefficient of graphene nanoribbon (GNR) is presented. The model is derived by calculating probability of electrons reaching ionization threshold energy E_t and the distance travelled by electron gaining E_t. In addition, ionization threshold energy is semi-analytically modelled for GNR. During modelling, we justify our assumptions using analytical modelling and comparison with simulation. Furthermore, it is shown that conventional silicon models are not valid for calculation of ionization coefficient of GNR. Finally, the profile of ionization is presented using the proposed models and the results are compared with that of silicon.     

Novel composite nanofibers based on polyamide 66/graphene oxide- grafted aliphatic- aromatic polyamide: preparation and characterization

New polyamide 66/graphene oxide (GO)-grafted aliphatic-aromatic polyamide (polyamide-imide) (PAI) (PA66/GOF) composites nanofibers were successfully prepared via electrospinning method for the first time. An polyamide imide (PAI) was synthesized using polycondensation reaction from a dicarboxylic acid and a diamine based on 4,4′-(4,4′-isopropylidenediphenyl-1,1′-diyldioxy) dianiline, and characterized by ¹HNMR and FTIR. Morphological, structural, thermal and mechanical characteristics of the nanocomposite fibers were investigated by means of SEM, TEM, WAXD, DMTA and TGA techniques. Composites nanofibers of PA66/GO, PA66/PAI and PA66/GOF with smooth surface, uniform structure as well as with diameter ranging from 195 to 784 nm were obtained. The GO incorporation caused a reduction in the nanofibers diameters. The TEM images showed that the GO was well dispersed in the PA66 nanofibers without significant aggregation. An approximately 10 °C temperature increase in the glass transition temperature of PA66 was achieved by addition of 0.5 wt% of PAI, resulting from aliphatic-aromatic structure of PAI. By the TGA results, an increase about 40 °C was observed in the thermal stability of PA66/PAI composite nanofibers in comparison with that of pure PA66 nanofibers.     

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

This study proposes a data‐driven operational control framework using machine learning‐based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi‐stage framework is composed of the following steps: (a) a clustering algorithm based on Density‐Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high‐accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data‐driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm³ of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.     

Direct extraction of Mo(VI) from sulfate solution by synergistic extractants in the rotation column

Direct extraction of molybdenum from sulfate solution with synergistic extractants (mixture of D₂EHPA and TBP) was studied in the rotation column. The influence of extractant concentration and initial pH of aqueous phase was studied in the bench scale experiments. The outcomes demonstrated that the synergistic solvent extraction enhances the constancy of the extracted complexes for transfer into the organic phase. In the continuous experiments, the effect of different operating parameters such as speed of agitation, inlet solvent flow rate and inlet aqueous flow rate on the holdup, mean drop size, drop size distribution, slip and characteristic velocities and extraction percentage were examined. Modified correlations were proposed for prediction of hydrodynamic parameters with consideration of reaction extraction condition in the rotation column. Furthermore, these correlations were compared with the experimental data. According to the results, the direct extraction of Mo (VI) from aqueous solution and sulfuric media with extraction efficiency of 90.4% was obtained at higher rotor speed (240 r·min^-1 rpm) in this column.     

Stochastic economic analysis of FACTS devices on contingent transmission networks using hybrid biogeography-based optimization

Electrical Engineering - Flexible AC transmission systems (FACTS) devices have advantages of enhancing AC system controllability and stability, increasing power transfer capability and relieving...