Live video streaming service with pay-as-you-use model on Ethereum Blockchain and InterPlanetary file system

Wireless Networks - In centralized video streaming platforms, the platform owner, rather than the content producer, controls most of the content uploaded on the centralized video...     

Synthetic,spectroscopic, magnetic,thermal, and antimicrobial approach towards new biocidal coordination polymers

O‐aminophenol was reacted with glutraldehyde to obtain Schiff base, which was then reacted with formaldehyde in slight acidic medium to generate phenolic groups. Now the substituted Schiff base was reacted with the transition metal acetates of Mn(II), Co(II), Ni(II), Cu(II), and Zn(II) to get polymeric metal complexes. Their structures have been elucidated on the basis of elemental analyses, ¹H NMR spectra, ¹³C NMR spectra, magnetic measurements, thermogravimetric analyses, electronic spectra, and infrared spectra. The results are in accordance with an octahedral environment around the central metal ion. The polychelates of Mn(II), Co(II), Ni(II), and Cu(II) are paramagnetic while Zn(II) polychelate was found to be diamagnetic. The synthesized Schiff base acted as a uninegative bidentate ligand and bonding occurs through the hydroxyl oxygen and nitrogen atoms. The thermal behavior of these coordinating polymers was studied by TGA in nitrogen atmosphere up to the temperature range of 800°C. All the synthesized polychelates were also screened for their biocidal activity against Escherichia coli, Staphylococcus aureus, Bacillus subtilis (bacteria), Candida albicans, and Muller species (yeast) by using agar well diffusion method. All the metal polychelates show promising antimicrobial activities. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci 124:3971–3979, 2012     

SAR image despeckling using heavy-tailed Burr distribution

Multiplicative speckle noise diminishes the radiometric resolution of the synthetic aperture radar (SAR) images and all the coherent images. Speckle removal adds an extra value to an automated SAR image interpretation and analysis. In this paper, dual-tree complex wavelet-transform-based Bayesian method is proposed for despeckling the SAR images. In each subband, the reflectance and noise of the logarithmically transformed wavelet coefficients are modeled using heavy-tailed Burr and zero-mean Gaussian distributions. The closed-form expression for the shape parameter of Burr distribution is derived by employing the Mellin transform. The resultant complex-free quadratic maximum a posteriori solution with suitable shrinkage function yields despeckled SAR images. Extensive experiments are carried out using real SAR images as well as simulated images. The proposed method performs well in terms of equivalent number of looks with 3.5751 dB improvement in homogeneous region1 of Pipe river SAR image, edge preservation with 0.6158 improvement, peak signal to noise ratio of 51.3305 dB, and mean structural similarity index measure of 0.9397 at 0.05 noise variance for synthetically speckled image in comparison to the existing methods and takes averagely 2.3461 times less computing time. The proposed method provides a computationally efficient better speckle reduction in homogeneous regions while still preserving the edge.     

Antagonism of the Stat3-Stat3 protein dimer with salicylic acid based small molecules

More than 50 new inhibitors of the oncogenic Stat3 protein were identified through a structure–activity relationship (SAR) study based on the previously identified inhibitor S3I‐201 (IC₅₀=86 μM , K_i>300 μM ). A key structural feature of these inhibitors is a salicylic acid moiety, which, by acting as a phosphotyrosine mimetic, is believed to facilitate binding to the Stat3 SH2 domain. Several of the analogues exhibit higher potency than the lead compound in inhibiting Stat3 DNA binding activity, with an in vitro IC₅₀ range of 18.7–51.9 μM , and disruption of Stat3–pTyr peptide interactions with K_i values in the 15.5–41 μM range. One agent in particular exhibited potent inhibition of Stat3 phosphorylation in both breast and multiple myeloma tumor cells, suppressed the expression of Stat3 target genes, and induced antitumor effects in tumor cells harboring activated Stat3 protein.     

Robust Attack Detection Approach for IIoT Using Ensemble Classifier

Generally, the risks associated with malicious threats are increasing for the Internet of Things (IoT) and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based intrusion detection models for IoT networks are vital. Distinct detection methodologies need to be developed for the Industrial Internet of Things (IIoT) network as threat detection is a significant expectation of stakeholders. Machine learning approaches are considered to be evolving techniques that learn with experience, and such approaches have resulted in superior performance in various applications, such as pattern recognition, outlier analysis, and speech recognition. Traditional techniques and tools are not adequate to secure IIoT networks due to the use of various protocols in industrial systems and restricted possibilities of upgradation. In this paper, the objective is to develop a two-phase anomaly detection model to enhance the reliability of an IIoT network. In the first phase, SVM and Naïve Bayes, are integrated using an ensemble blending technique. K-fold cross-validation is performed while training the data with different training and testing ratios to obtain optimized training and test sets. Ensemble blending uses a random forest technique to predict class labels. An Artificial Neural Network (ANN) classifier that uses the Adam optimizer to achieve better accuracy is also used for prediction. In the second phase, both the ANN and random forest results are fed to the model’s classification unit, and the highest accuracy value is considered the final result. The proposed model is tested on standard IoT attack datasets, such as WUSTL_IIOT-2018, N_BaIoT, and Bot_IoT. The highest accuracy obtained is 99%. A comparative analysis of the proposed model using state-of-the-art ensemble techniques is performed to demonstrate the superiority of the results. The results also demonstrate that the proposed model outperforms traditional techniques and thus improves the reliability of an IIoT network.     

Exploring the optical properties of exposed-core-based photonic-crystal fibers

Journal of Computational Electronics - A photonic-crystal fiber (PCF) based on the surface plasmon resonance (SPR) effect with low confinement loss and high sensitivity response is designed and its...     

New antimicrobial polyurea: Synthesis,characterization, and antibacterial activities of polyurea‐containing thiosemicarbazide–metal complexes

A novel class of polymer–metal complexes was prepared by the condensation of a polymeric ligand with transition‐metal ions. The polymeric ligand was prepared by the addition polymerization of thiosemicarbazides with toluene 2,4‐diisocyanate in a 1 : 1 molar ratio. The polymeric ligand and its polymer–metal complexes were characterized by elemental analysis, thermogravimetric analysis, Fourier transform infrared spectroscopy, and ¹³C‐NMR and ¹H‐NMR spectroscopy. The geometries of the central metal ions were determined by electronic spectra (UV–visible) and magnetic moment measurement. The antibacterial activities of all of the synthesized polymers were investigated against Bacillus subtilis and Staphylococcus aureus (Gram positive) and Escherichia coli and Salmonella typhi (Gram negative). These compounds showed excellent antibacterial activities against these bacteria with the spread plate method on agar plates, and the number of viable bacteria were counted after 24 h of incubation period at 37°C. The antibacterial activity results revealed that the Cu(II) chelated polyurea showed a higher antibacterial activity than the other metal‐chelated polyureas. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Effect of Solvent Concentration and Cooling Modes on Morphology,Particle Size Distribution,and Yield of Dihydroxystearic Acid (DHSA) Crystals

Palm-based dihydroxystearic acid was purified by employing solvent crystallization using ethanol and ethanol/water mixture as solvents. Two different cooling modes, rapid and natural cooling, were employed for the crystallization process. In this study, the cooling effects on morphology, habit, particle size distribution, and yield of dihydroxystearic acid crystals were investigated. The dihydroxystearic acid crystals appeared to agglomerate into sphere-like and plate-like structures, depending on the cooling modes and the presence of water. By manipulating the solvent and cooling modes, the crystal yield ranged from 0.8% to 42.1%, while the crystal purity ranged from 73.3% to 91.4%. Solvent crystallization using ethanol was best achieved through natural cooling with the dihydroxystearic acid to ethanol ratio of 1.0:1.0, which produced the narrowest particle size distribution curve with the largest average particle size.