Designing Cellulose Acetate - Polyacrylamide Semi-Interpenetrating Polymer Networks and Evaluation of their Protein Retention Behavior

In this work semi-IPNs of cellulose acetate (CA) – N, N’- methylene bisacrylamide (MBA) – crosslinked polyacrylamide (PAM), and native CA gel were prepared and characterized by FTIR, AFM, SEM, XRD, TGA and DSC techniques. The AFM studies revealed that addition of AM increased the symmetry of the semi-IPN surfaces whereas the XRD spectra suggested for a decrease in crystalline nature of CA. The network parameters were changed with change in concentrations of CA and AM. The prepared semi-IPNs were examined for retention of bovine serum albumin (BSA). The mechanical properties, swelling capacity, % porosity and biocompatibility were also investigated.     

Joint power allocation and relay selection strategy for 5G network: a step towards green communication

Green communication has emerged as the most important concept for the next generation networks. Along with improved data rate and capacity, the upcoming 5G networks aim at improving energy efficiency without compromising on the user experience. In this paper, we have used amplify and forward relays in the heterogeneous network topology consisting of low power and high power nodes. A three layered system model for power optimization is discussed using a relay selection strategy for power optimization with the aim to improve energy efficiency of the network. Further, we have used Hidden Markov Model for training and maintaining of base station, relay and SCA with the aim of probabilistic power allocation to client nodes in order to solve the power optimization problem. We have also used adaptive modulation schemes for lowering the power consumption of the network to meet our goal of green communication for the next generation network.     

Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

The fault diagnosis in wireless sensor networks is one of the most important topics in the recent years of research work. The problem of fault diagnosis in wireless sensor network can be resembled with artificial immune system in many different ways. In this paper, a detection algorithm has been proposed to identify faulty sensor nodes using clonal selection principle of artificial immune system, and then the faults are classified into permanent, intermittent, and transient fault using the probabilistic neural network approach. After the actual fault status is detected, the faulty nodes are isolated in the isolation phase. The performance metrics such as detection accuracy, false alarm rate, false‐positive rate, fault classification accuracy, false classification rate, diagnosis latency, and energy consumption are used to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm gives superior results as compared with existing algorithms in terms of the performance metrics. The fault classification performance is measured by fault classification accuracy and false classification rate. It has also seen that the proposed algorithm provides less diagnosis latency and consumes less energy than that of the existing algorithms proposed by Mohapatra et al, Panda et al, and Elhadef et al for wireless sensor network.     

Computationally efficient variational Bayesian method for PAPR reduction in multiuser MIMO‐OFDM systems

This paper investigates the use of the inverse‐free sparse Bayesian learning (SBL) approach for peak‐to‐average power ratio (PAPR) reduction in orthogonal frequency‐division multiplexing (OFDM)‐based multiuser massive multiple‐input multiple‐output (MIMO) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought‐after low‐PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation‐maximization (EM) iterative algorithm. The matrix inversion involved in the expectation step (E‐step) is averted by invoking a relaxed evidence lower bound (relaxed‐ELBO). The resulting inverse‐free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations.     

Review of scientific technology-based solutions for vehicular pollution control

Clean Technologies and Environmental Policy - Numerous management strategies are implemented for the improvement in urban air quality worldwide, including control at tailpipe emissions. Control at...     

Designing a block stacked warehouse for dynamic and stochastic product flow: a scenario-based robust approach

The problem considered in this paper is motivated from a real life case. Conventional models in designing block stacked warehouse assume uniform and deterministic inflow and outflow of products in specific quantities and time intervals. These assumptions would lead to underestimation of space requirement for the case. Also, the possibility of leasing additional warehouse space during seasonal spikes was found to be unexplored in block stacking design literature. In this study, we develop a scenario-based model to address the decisions of warehouse size, lane depth and hiring of temporary additional warehouse capacity when flow of products is stochastic and dynamic in nature. A sequential decision model is presented with future operating scenarios assumed to branch out in the form of a tree. On comparison with conventional block stacking design methods, the model is found to provide robust solution in stochastic and dynamic environments in terms of risk reduction and infeasibility avoidance. Further, the model gives additional flexibility to the designer to trade-off model and solution robustness with predicted cost.     

Low-temperature synthesis of manganese oxide–carbon nanotube-enhanced microwave-absorbing nanocomposites

Noting that the dielectric properties of manganese oxide make it a promising microwave-absorbing material, a low-temperature method to deposit crystalline MnO₂ over carbon nanotubes (CNTs) is developed. Adjusting the pH of the precursor solution allows control over the phases and morphologies of the synthesized manganese oxides MnO₂ and Mn₃O₄ that have minimum reflection losses of ??11 dB and ??6 dB, respectively. The synthesized CNT–MnO₂ and CNT–Mn₃O₄ nanocomposites are superior microwave absorbers than simpler physical mixtures of CNTs and manganese oxides, with reflection losses as high as ??19 dB at 9.5 GHz and ??34 dB at 4 GHz, and have wider absorption bands than pure manganese oxides. Coating CNTs with manganese oxide not only increases dielectric and magnetic losses, but also improves the impedance match between free space and the absorber. The addition of CNTs to pure MnO₂ and Mn₃O₄ improves impedance matching by enhancing the relaxation polarization and conductivity losses, magnetic loss, including contributions form eddy current and natural resonance. This facile, low-cost, scalable, high-yield method produces an enhanced microwave-absorbing nanocomposite.     

Automatic PV Grid Fault Detection System with IoT and LabVIEW as Data Logger

Fault detection of the photovoltaic (PV) grid is necessary to detect serious output power reduction to avoid PV modules’ damage. To identify the fault of the PV arrays, there is a necessity to implement an automatic system. In this IoT and LabVIEW-based automatic fault detection of 3 × 3 solar array, a PV system is proposed to control and monitor Internet connectivity remotely. Hardware component to automatically reconfigure the solar PV array from the series-parallel (SP) to the complete cross-linked array underneath partial shading conditions (PSC) is centered on the Atmega328 system to achieve maximum power. In the LabVIEW environment, an automated monitoring system is developed. The automatic monitoring system assesses the voltage drop losses present in the DC side of the PV generator and generates a decimal weighted value depending on the defective solar panels and transmits this value to the remote station through an RF modem, and provides an indicator of the faulty solar panel over the built-in Interface LabVIEW. The managing of this GUI indicator helps the monitoring system to generate a panel alert for damaged panels in the PV system. Node MCU in the receiver section enables transmission of the fault status of PV arrays via Internet connectivity. The IoT-based Blynk app is employed for visualizing the fault status of the 3 × 3 PV array. The dashboard of Blynk visualizes every array with the status.