Synthesis of urea-modified MnFe<Subscript>2</Subscript>O<Subscript>4</Subscript> for aromatic micro-pollutants adsorption from wastewater: Mechanism and modeling

Amine-terminated MnFe₂O₄, using urea solution (UMF-MNPs) fabricated by a combined ultrasonic/microwave technique, was characterized and applied as a magnetic adsorbent to remove catechol, phenol, and benzene micro-pollutants from wastewater solutions. The simultaneous effect of solution pH, TDS, micro-pollutants concentrations, UMF-MNPs dose, and temperature was investigated and optimized using D-optimal design in a batch experiment. The quadratic regression, as the best-structured simulated model, predicted the maximum adsorption rates at pH 6.0, TDS 1855 mg/l, UMF-MNPs dose of 0.73 g/l, and temperature 320 K for 50 mg/l micro-pollutant concentrations. Kinetic and isotherm models, through linear and nonlinear regression methods, reflected an excellent correlation (R ² ≈ 0.83 to 1) to simulate the adsorption data. The column breakthrough curves were tested using the equivalent length of the unused bed approach, which well fitted the Thomas model equation. The film diffusion, consequently with Lewis acid–base, is the major rate-controlling adsorption mechanism, and recovered by alkali ethanolic solution.     

In-Band Device to Device (D2D) Communication and Device Discovery: A Survey

Wireless Personal Communications - Device to device (D2D) communication is one of the potentials to achieve the established standards for 5G. It represents a direct communication between two...     

Autoregressive modeling of speech trajectory transformed to the reconstructed phase space for ASR purposes

Investigating new effective feature extraction methods applied to the speech signal is an important approach to improve the performance of automatic speech recognition (ASR) systems. Owing to the fact that the reconstructed phase space (RPS) is a proper field for true detection of signal dynamics, in this paper we propose a new method for feature extraction from the trajectory of the speech signal in the RPS. This method is based upon modeling the speech trajectory using the multivariate autoregressive (MVAR) method. Moreover, in the following, we benefit from linear discriminant analysis (LDA) for dimension reduction. The LDA technique is utilized to simultaneously decorrelate and reduce the dimension of the final feature set. Experimental results show that the MVAR of order 6 is appropriate for modeling the trajectory of speech signals in the RPS. In this study recognition experiments are conducted with an HMM-based continuous speech recognition system and a naive Bayes isolated phoneme classifier on the Persian FARSDAT and American English TIMIT corpora to compare the proposed features to some older RPS-based and traditional spectral-based MFCC features.     

Correlation between the dimensionality and the constants of elasticity of rare-earth doped borate glasses

This work was devoted to explore the correlation between the dimensionality and the computed theoretically constants of elasticity of borate based glasses doped with rare-earth oxides. The dimensionality of the glassy network has been calculated in terms of the d ratio which is equal to 4 C ₄₄/K _e and discussed in terms of the cross-link density and number of network bonds per unit volume of these glasses. Constants of elasticity were calculated in terms of the bond compression model and the Makishima-Mackenzie model. The average cross-link density, the number of network bonds per unit volume, the average stretching-force constant, and the ratio of the estimated bulk modulus (K _bc) to the experimentally determined (K _e) have been calculated and discussed in terms of the bond-compression model. Young??s modulus, the packing density, and the dissociation energy have been calculated and analyzed in terms of the Makishima-Mackenzie model. The results showed that the computed elastic moduli and the dimensionality of the borate glasses containing La₂O₃ or Gd₂O₃ are strongly dependent on the concentration of the structural units of the constituent oxides and types of bonds between these units.     

Influence of the pH on the Morphology of Sol–Gel-Derived Nanostructured SiC

This investigation deals with the use of the carbothermal reduction reaction of carbonaceous silica xerogel for synthesizing nanostructured SiC. The effect of the pH of the early-prepared gel (EPG) on the properties of the finally obtained nanostructured silicon carbide was investigated. The produced silicon carbide samples were characterized by X-ray diffraction, fourier transform infrared spectroscopy, transmission electron microscopy, and N₂ porosimetry. The results revealed that the variation of EPG pH greatly affected the structural properties of the finally obtained nanostructured silicon carbide. The morphology of the produced nanostructured silicon carbide was changed from nanoparticles to nanorods and vice versa with changing the pH values of the gel. The nitrogen adsorption–desorption isotherms of the samples are of classical type IV, characteristic of mesoporous materials. The BET surface areas of the nanostructured silicon carbide are increased from 68 to 106 m²/g with increasing the pH of the EPG from 3 to 5, respectively.     

Medical Diagnosis Using Machine Learning: A Statistical Review

Decision making in case of medical diagnosis is a complicated process. A large number of overlapping structures and cases, and distractions, tiredness, and limitations with the human visual system can lead to inappropriate diagnosis. Machine learning (ML) methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis. Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published. Hence, to determine the use of ML to improve the diagnosis in varied medical disciplines, a systematic review is conducted in this study. To carry out the review, six different databases are selected. Inclusion and exclusion criteria are employed to limit the research. Further, the eligible articles are classified depending on publication year, authors, type of articles, research objective, inputs and outputs, problem and research gaps, and findings and results. Then the selected articles are analyzed to show the impact of ML methods in improving the disease diagnosis. The findings of this study show the most used ML methods and the most common diseases that are focused on by researchers. It also shows the increase in use of machine learning for disease diagnosis over the years. These results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.     

Color classification using color vision models

According to recent physiological research results, there are many individual differences already at the detection level of our color vision system. It is not completely clear yet, how the other levels of color vision system compensate the detection differences. Instead of detecting and analyzing colors exactly in the same way, we all just have learned to classify colors in a certain way, which seems to lead almost always to the same result independent of the individual differences in the color vision system. In this article, we experiment with four models developed for replicating certain properties of human color vision. We examine the color classification abilities of these models and show the differences and similarities in their behavior. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010.     

The synthesis of multilayer graphene materials by the fluorination of carbon nanodiscs/nanocones

Multilayer carbonaceous nanomaterial has been synthesized using a two-step process: carbon nanodiscs/nanocones were fluorinated using either the direct reaction with pure F₂ gas or the thermal decomposition of solid fluorinating agent (TbF₄). Then the fluorinated parts were removed by treatment at 600 °C in air. When the fluorine atoms are homogenously dispersed, using fluorination by TbF₄, thinning due to thermal defluorination results in multilayer materials with 7–10 nm of thickness and 400–500 nm of width. Such resulting materials and the fluorinated precursors have been characterized by solid state NMR, TGA, XRD, SEM, TEM, AFM and Raman spectroscopy.     

Polarization-discrimination technique to maximize the lidar signal-to-noise ratio for daylight operations

The impact and potential of a polarization-selection technique to reduce the sky background signal for linearly polarized monostatic elastic backscatter lidar measurements are examined. Taking advantage of naturally occurring polarization properties in scattered skylight, we devised a polarization-discrimination technique in which both the lidar transmitter and the receiver track and minimize detected sky background noise while maintaining maximum lidar signal throughput. Lidar elastic backscatter measurements, carried out continuously during daylight hours at 532 nm, show as much as a factor of square root 10 improvement in the signal-to-noise ratio (SNR) over conventional unpolarized schemes. For vertically pointing lidars, the largest improvements are limited to the early morning and late afternoon hours, while for lidars scanning azimuthally and in elevation at angles other than vertical, significant improvements are achievable over more extended time periods with the specific times and improvement factors depending on the specific angle between the lidar and the solar axes. The resulting diurnal variations in SNR improvement sometimes show an asymmetry with the solar angle that analysis indicates can be attributed to changes in observed relative humidity that modifies the underlying aerosol microphysics and observed optical depth.