Synthesis,characterization and electrical properties of the system LaMnxFe1−xO3

The system manganese substituted lanthanum ferrite viz. LaMn_xFe_1?xO₃ (1.0 ≤ x ≥ 0) was prepared by sol–gel autocombusion method. The structural characterization of the samples was carried out by X-ray diffraction technique and it is found that, the phase transfer from cubic to orthorhombic perovskite structure. The lattice parameter and crystallite size decrease with increasing Mn content. The phase formation of perovskite was revealed by thermal analysis technique. The surface morphology and elemental analysis of all the samples were carried out by scanning electron microscopy and energy dispersive X-ray spectroscopic technique, respectively. Electrical properties of the compounds show that, they exhibit semiconducting behavior. The substitution of manganese ions plays an important role in changing their structural, electrical and magnetic properties of lanthanum ferrite.     

Biocompatible tumor micro-environment responsive CS-g-PNIPAAm co-polymeric nanoparticles for targeted Oxaliplatin delivery

The objective of the research study was to develop and characterize a biodegradable, thermo and pH dual responsive Oxaliplatin-loaded chitosan-graft-poly-N-isopropylacrylamide (CS-g-PNIPAAm) co-polymeric nanoparticles as a tumor-targeting drug delivery system. CS-g-PNIPAAm co-polymers were synthesized, characterized and optimized its thermo and pH responsive properties for tumor microenvironment conditions. Optimized co-polymer could be efficiently loaded with Oxaliplatin in nanoparticle form, evaluated for their morphology (TEM), particle size, zeta potential, loading efficiency and drug content. In vitro drug release study at tumor microenvironment and physiological pH and temperature conditions. The in vitro drug release was optimal at above lower critical solution temperature (LCST) and tumor microenvironment pH when compared to physiological pH & temperature. MTT assay and fluorescence microscopic study showed that drug release and cell uptake was significantly enhanced in tumor microenvironment. In conclusion, the obtained nanoparticles appeared to be of great promise in tumor targeted drug delivery of oxaliplatin.     

Molecular Insights on the Possible Role of Annexin A2 in COVID-19 Pathogenesis and Post-Infection Complications

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has infected >235 million people and killed over 4.8 million individuals worldwide. Although vaccines have been developed for prophylactic management, there are no clinically proven antivirals to treat the viral infection. Continuous efforts are being made all over the world to develop effective drugs but these are being delayed by periodic outbreak of mutated SARS-CoV-2 and a lack of knowledge of molecular mechanisms underlying viral pathogenesis and post-infection complications. In this regard, the involvement of Annexin A2 (AnxA2), a lipid-raft related phospholipid-binding protein, in SARS-CoV-2 attachment, internalization, and replication has been discussed. In addition to the evidence from published literature, we have performed in silico docking of viral spike glycoprotein and RNA-dependent RNA polymerase with human AnxA2 to find the molecular interactions. Overall, this review provides the molecular insights into a potential role of AnxA2 in the SARS-CoV-2 pathogenesis and post-infection complications, especially thrombosis, cytokine storm, and insulin resistance.     

Modified Two-Phase Titration Methods to Quantify Surfactant Concentrations in Chemical-Enhanced Oil Recovery Applications

In enhanced oil recovery applications, surfactants are injected into reservoirs along with polymers and salts. The effluents eluted from lab experiments and field tests are analyzed by HPLC methods using an evaporative light scattering detector (ELSD) detector. When the surfactant concentrations are less than 100 ppm, HPLC methods are inaccurate. A novel two-phase titration method is developed where surfactant concentrations can be quantified using a calibration curve constructed with UV/vis absorption. This method can analyze surfactant concentrations 5–80 ppm where dilution eliminates any high-salinity interferences with the absorption measurements. The method is based on formation of a dye-surfactant complex and the light absorption of the complex has a linear correlation with the surfactant concentration. Anionic surfactant concentrations lower than 100 ppm can be accurately quantified using this method with methylene blue. The method was also developed for low concentrations (<50 ppm) of cationic surfactants using methyl orange and indigo carmine. The indigo carmine method can be used without the use of an organic phase. All methods are applicable at salinities up to 3 wt%. Both the methylene blue method and the methyl orange method can be used to detect zwitterionic surfactants. These methods can be used in the presence of polymers without any prior treatments.     

Surface Binding of TOTAPOL Assists Structural Investigations of Amyloid Fibrils by Dynamic Nuclear Polarization NMR Spectroscopy

Dynamic nuclear polarization (DNP) NMR can enhance sensitivity but often comes at the price of a substantial loss of resolution. Two major factors affect spectral quality: low‐temperature heterogeneous line broadening and paramagnetic relaxation enhancement (PRE) effects. Investigations by NMR spectroscopy, isothermal titration calorimetry (ITC), and EPR revealed a new substantial affinity of TOTAPOL to amyloid surfaces, very similar to that shown by the fluorescent dye thioflavin‐T (ThT). As a consequence, DNP spectra with remarkably good resolution and still reasonable enhancement could be obtained at very low TOTAPOL concentrations, typically 400 times lower than commonly employed. These spectra yielded several long‐range constraints that were difficult to obtain without DNP. Our findings open up new strategies for structural studies with DNP NMR spectroscopy on amyloids that can bind the biradical with affinity similar to that shown towards ThT.     

Passenger flow prediction in bus transportation system using deep learning

The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.

     

Simultaneous automated design of structured QFT controller and prefilter using nonlinear programming

This paper describes a nonlinear programming‐based robust design methodology for controllers and prefilters of a predefined structure for the linear time‐invariant systems involved in the quantitative feedback theory. This controller and prefilter synthesis problem is formulated as a single optimization problem with a given performance optimization objective and constraints enforcing stability and various specifications usually enforced in the quantitative feedback theory. The focus is set on providing constraints expression that can be used in standard nonlinear programming solvers. The nonlinear solver then computes in a single‐step controller and prefilter design parameters that satisfy the prescribed constraints and maximizes the performance optimization objective. The effectiveness of the proposed approach is demonstrated through a variety of difficult design cases like resonant plants, open‐loop unstable plants, and plants with variation in the time delay. Copyright © 2016 John Wiley & Sons, Ltd.     

K-DDoS-SDN: A distributed DDoS attacks detection approach for protecting SDN environment

Software-defined networking (SDN) is an advanced networking paradigm that decouples forwarding control logic from the data plane. Therefore, it provides a loosely-coupled architecture between the control and data plane. This separation provides flexibility in the SDN environment for addressing any transformations. Further, it delivers a centralized way of managing networks due to control logic embedded in the SDN controller. However, this advanced networking paradigm has been facing several security issues, such as topology spoofing, exhausting bandwidth, flow table updating, and distributed denial of service (DDoS) attacks. A DDoS attack is one of the most powerful menaces to the SDN environment. Further, the central data controller of SDN becomes the primary target of DDoS attacks. In this article, we propose a Kafka-based distributed DDoS attacks detection approach for protecting the SDN environment named K-DDoS-SDN. The K-DDoS-SDN consists of two modules: (i) Network traffic classification (NTClassification) module and (ii) Network traffic storage (NTStorage) module. The NTClassification module is the detection approach designed using scalable H2O ML techniques in a distributed manner and deployed an efficient model on the two-nodes Kafka Streams cluster to classify incoming network traces in real-time. The NTStorage module collects raw packets, network flows, and 21 essential attributes and then systematically stores them in the HDFS to re-train existing models. The proposed K-DDoS-SDN designed and evaluated using the recent and publically available CICDDoS2019 dataset. The average classification accuracy of the proposed distributed K-DDoS-SDN for classifying network traces into legitimate and one of the most popular attacks, such as DDoS_UDP is 99.22%. Further, the outcomes demonstrate that proposed distributed K-DDoS-SDN classifies traffic traces into five categories with at least 81% classification accuracy.     

Spectral analysis of infant cries and adult speech

In this paper, spectrographic analysis of the infant cries is reported. For the spectrographic analysis of the infant cries ten different cry modes are used to analyze differences in different pathological cries. A comparison of spectrograms of the adult speech signal and infant cry signals is given. Based on differences in the distribution of energy in the spectrograms, energy-based features are calculated from the short-time Fourier transform (STFT) of the adult speech and infant cry signals. The classification performance of these features is obtained using support vector machine (SVM) classifier and it is observed that the energy distribution in 0–1 kHz range is promising feature in the classification of adult speech and infant cries and the classification accuracy achieved with this feature is 98.22 %. On the contrary, it was observed that it is very difficult to classify adult speech and infant cries using the energy distribution in 1–3 kHz.