Aggregation and adsorption behavior of cobalt-based metallosurfactant in water–ethylene glycol media forming worm-like micelles

The cobalt based metallosurfactant cis-chlorobis(ethylenediamine)hexadecylaminecobalt(III) chloride (CHCC) has been prepared and well characterized by utilizing elemental analysis, NMR (¹H, ¹³C), FT-IR and UV–Vis spectroscopy. The CHCC metallosurfactant shows thermal stability up to 168°C. The micellization behavior of the synthesized CHCC metallosurfactant has been investigated systematically by the tensiometric, conductometric, and fluorescence techniques. The critical micelle concentration (cmc) values of CHCC have been determined in various water–ethylene glycol mixtures ranging from 0 to 100 weight % of ethylene glycol at 293.15, 298.15, 303.15, and 308.15 K. The physicochemical parameters namely counterion binding constant, surface pressure, surface excess, surface area covered per CHCC metallosurfactant molecule, free energy minimum, standard free energies of micellization and adsorption, standard enthalpy and entropy of micellization, and Gibb's free energy of transfer have been calculated. The hydrodynamic diameters and zeta potentials of the CHCC metallomicelles have been measured by dynamic light scattering method. Transmission electron microscopy was employed to confirm the presence of worm-like micelles.     

A Software-Defined Network-based Intelligent Decision Support System for the Internet of Things Networks

Wireless Personal Communications - The concept of connected devices is effectively integrated to create heterogeneous wireless networks like the Internet of Things (IoT). These networks are using...     

Advances in Chemically Powered Micro/Nanorobots for Biological Applications: A Review

Micro/nanorobots (MNRs) are capable of autonomous motion, breaking through the limitations of traditional passive transport of nanocarriers. Among them, chemically driven MNRs are the earliest MNRs studied and have received extensive attention from researchers. This review first focuses on the material properties, preparation, driving forms, and mechanisms of chemically driven MNRs. The current status of research on chemically driven MNRs in biomedicine is summarized for various biological applications (drug delivery, diagnostics, anti-inflammatory, antibacterial, and disease treatment). In terms of biosafety, possible safety issues are analyzed in the context of chemically driven microrobotic applications in terms of three aspects: component characteristics, chemical engines and biological environment. Finally, the challenges and possible future directions of chemically driven MNRs are presented.     

Machine Learning with Dimensionality Reduction for DDoS Attack Detection

With the advancement of internet, there is also a rise in cybercrimes and digital attacks. DDoS (Distributed Denial of Service) attack is the most dominant weapon to breach the vulnerabilities of internet and pose a significant threat in the digital environment. These cyber-attacks are generated deliberately and consciously by the hacker to overwhelm the target with heavy traffic that genuine users are unable to use the target resources. As a result, targeted services are inaccessible by the legitimate user. To prevent these attacks, researchers are making use of advanced Machine Learning classifiers which can accurately detect the DDoS attacks. However, the challenge in using these techniques is the limitations on capacity for the volume of data and the required processing time. In this research work, we propose the framework of reducing the dimensions of the data by selecting the most important features which contribute to the predictive accuracy. We show that the ‘lite’ model trained on reduced dataset not only saves the computational power, but also improves the predictive performance. We show that dimensionality reduction can improve both effectiveness (recall) and efficiency (precision) of the model as compared to the model trained on ‘full’ dataset.     

Effect of Ho3+ ions on microwave losses and high-temperature electrical behavior of Li-based magnetic oxides

We investigate here the effect of holmium on Li–Co nano-ferrites to elaborate the surface morphology, dynamic magnetic and electrical transport properties. The transmission electron microscopy (TEM) technique was employed to examine the microstructure and grain size distribution. TEM analysis confirmed the nanocrystalline nature (~50 nm) of the prepared materials. X-ray photoelectron spectroscopy (XPS) experiment results verify the presence of all metal ions with the required valences. Ferromagnetic resonance (FMR) analysis revealed the need for a dense microstructure to cut down the microwave losses. FMR line width was observed to reduce from 2757 -to 1676 Oe except for x = 0.12 by the substitution of Ho ions which correspond to low microwave losses. The dc resistivity results show that high resistivity values are associated with smaller grains of the samples and vice versa. Resistivity values are found to increase from 3.66 × 10⁸ -to 5.31 × 10⁸ Ω-cm by increasing the Ho addition. Seebeck experiment revealed n-type conduction. Together with showing the nature of charge carriers, a decrease in the Seebeck coefficient with increasing Ho ensured the replacement of Fe ions by Ho ions on B-sites.     

Ternay Au@TiO2/α-Fe2O3 Nanocomposite with Nanoring Structure: Synthesis,Characterization and Photocatalytic Activity

Journal of Inorganic and Organometallic Polymers and Materials - In the present study, a modified solvothermal reaction of (hematite) with titanium(IV) butoxide and gold(III) chloride produced...     

Pressure mode decomposition analysis of the flow past a cross-flow oscillating circular cylinder

Journal of Mechanical Science and Technology - Proper orthogonal decomposition (POD) is often employed in developing reduced-order models (ROM) in fluid flows for design, control, and optimization....     

Optimization of hydrogen production via toluene steam reforming over Ni–Co supported modified-activated carbon using ANN coupled GA and RSM

Hydrogen (H₂) is a clean fuel that can be produced from various resources including biomass. Optimization of H₂ production from catalytic steam reforming of toluene using response surface methodology (RSM) and artificial neural network coupled genetic algorithm (ANN-GA) models has been investigated. In RSM model, the central composite design (CCD) is employed in the experimental design. The CCD conditions are temperature (500–900 °C), feed flow rate (0.006–0.034 ml/min), catalyst weight (0.1–0.5 g) and steam-to-carbon molar ratio (1–9). ANN model employs a three-layered feed-forward backpropagation neural network in conjugation with the tangent sigmoid (tansig) and linear (purelin) as the transfer functions and Levenberg-Marquardt training algorithm. Best network structure of 4-14-1 is developed and utilized in the GA optimization for determining the optimum conditions. An optimum H₂ yield of 92.6% and 81.4% with 1.19% and 6.02% prediction error are obtained from ANN-GA and RSM models, respectively. The predictive capabilities of the two models are evaluated by statistical parameters, including the coefficient of determination (R²) and root mean square error (RMSE). Higher R² and lower RSME values are reported for ANN-GA model (R² = 0.95, RMSE = 4.09) demonstrating the superiority of ANN-GA in determining the nonlinear behavior compared to RSM model (R² = 0.87, RMSE = 6.92). These results infer that ANN-GA is a more reliable and robust predictive steam reforming modelling tool for H₂ production optimization compared to RSM model.     

Comments on `An algorithm for characteristic input-output modes ofdiscrete convolution systems' [with reply]

It is pointed out that an algorithm presented as original work in a previously published paper (see ibid., vol.32, no.1, p.67-9, 1987) was given in an earlier paper by B. Kouvaritakis and D.N. Kleftouris (Int. J. Contr., vol.31, no.1, p.127-52, 1980). In a reply, it is claimed that there is a significant difference between the two algorithms     

A solution technique for indefinite systems of mixed finite elements

A solution technique for indefinite systems of symmetric linear, simultaneous equations, via the Hellinger–Reissner variational principle, is presented. The method utilizes symmetry of the global matrix and its expected real eigenvalues. Premultiplication of the global matrix with itself renders a positive definite matrix, hence enabling the use of any standard equation solver for a positive definite system and requiring only about twice the memory requirement for the original set of equations. Two subroutines, MULT and MULTIP, which are compatible with the sky-line technique, are also listed.