Optimal monolithic configuration for heat integrated ethanol steam reformer

A design concept for optimal design of monolith catalyst is presented through modeling of transport–kinetic interactions in a monolith catalyst. We argue that reactors employing monolithic catalysts should be based on its optimal choice of geometry. In line with that argument, we present a thorough analysis of the geometrical parameters influencing the performance of non-isothermal reactor operation. In this study, an optimal monolith configuration is estimated to be a combination (d_h, t_w) of (0.9 mm, 0.2 mm) for a compact ethanol reformer to produce hydrogen for portable applications where maximum volumetric reactor activity exists. A three-dimensional modeling framework is developed for the resulting optimal monolithic catalyst design that couples the reforming section with a suitable heat source in a recuperative way. As a result, greater ethanol conversion is obtained from the monolith channels near the periphery of the block. The coupling with combustion could predict the formation of cold and hot spots inside the reactor, their nature being dependent on the flow configuration. Further, the effect of altering the feed inlet operating conditions over the variation of ethanol conversion and temperature inside the reactor is also analyzed. The increase in reforming inlet velocity decreases the outlet conversion and shifts the cold spot, forward and deeper in co-flow configuration. The decreasing inlet feed temperature enhances the transfer of heat, eliminating the cold spot.     

Efficient prediction of drug–drug interaction using deep learning models

A drug–drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug–drug interaction is defined as an ill‐posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug–drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug–drug interaction score efficiently. However, these models suffer from the over‐fitting issue. Therefore, these models are not so‐effective for predicting the drug–drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.Inspec keywords: cancer, learning (artificial intelligence), drugs, recurrent neural nets, convolutional neural nets, drug delivery systemsOther keywords: drug synergy, drug–drug interaction score, drug–drug interaction prediction, deep learning, cancer treatment, machine learning, convolutional mixture density recurrent neural network     

A secure multifactor remote user authentication scheme for Internet of Multimedia Things environment

To attain ubiquitous connectivity of everything, Internet of Things (IoT) systems must include “multimedia things.” Internet of Multimedia Things (IoMT) is a heterogeneous network of smart multimedia things connected together and with other physical devices to the Internet so as to achieve globally available multimedia services and applications. Due to the ever increasing amount of multimedia data in IoT environments, securing these systems becomes crucial. This is because these systems are easily susceptible to attacks when information or any service is accessed by the users. In this paper, we propose a secure three‐factor remote user authentication scheme for IoMT systems using ECC. The formal security proof performed using ROR model and BAN logic confirms that an attacker will not be able to extract sensitive user information. Through informal security analysis, we justify the resistance of the scheme against several security attacks. The performance comparison shows that the scheme is efficient in terms of computational cost, security features, and attack resistance. Furthermore, simulation of the scheme using AVISPA and Proverif proves that the scheme is secure against all active and passive attacks.     

Adaptive MAC Protocol for Solar Energy Harvesting Based Wireless Sensor Networks in Agriculture

The wireless sensor network is one of the promising technologies in the agriculture field. Its actual usage in real agriculture fields is limited by its dependence on the small batteries which cannot make the network survive for long. Various protocols are being designed at the network and MAC layer to increase the lifetime of the nodes, but up to a certain extent only. Hence the energy harvesting to power up the WSN nodes is a promising technology to fulfill this ever energy demand, but the protocols need to be redesigned for this scenario. Solar energy harvesting based MAC protocol which is adaptive to the changing weather conditions is designed in this paper for the smart agriculture applications. It is based on the multilayer and receiver-initiated process to improve network quality. It has shown the remarkable performance over the other energy harvesting based protocols in terms of ENO ratio, energy consumption and collision rate.

     

Enhancing linearity in I–V characteristics by B/N doping in graphene for communication devices

To explore communication applications, a study towards achieving linearity in the I–V characteristics through increasing concentrations of Boron (B) / Nitrogen (N) doping on pristine graphene sheet is investigated. Individual B/N doping of 6.25, 12.50, 18.75 and 25% has been done in the same sub lattice using Density Functional Theory (DFT) along with Non Equilibrium Greens Function (NEGF) calculations. The modification in the electronic and transport properties of graphene sheet are also investigated. In comparison to the variation of band gap from 0.35 to 1.183 eV and 0.36 to 1.149 eV for B and N respectively, an insignificant variation in effective mass is reported. Apart from linearity, variation in conductance in doped structures is seen. B doping increases conductivity and yields ON current of 610 µA while N doping gives ON current of 310 µA for maximum doping concentrations. In this work, the sustained carrier mobility and high gain linear characteristics of doped graphene obtained will help to utilise a graphene channel for different communication device applications.     

On the detection of Cardiac Arrhythmia with Principal Component Analysis

Wireless Personal Communications - The Electrocardiogram (ECG) signal is used to record the electrical activity of heart. The subtle variations in ECG attributes are used by cardiologists for...     

Rapid detection and quantification of soya bean oil and common sugar in bovine milk using attenuated total reflectance–fourier transform infrared spectroscopy

Attenuated total reflectance–Fourier transform infrared spectroscopy, along with chemometrics, were used to detect and quantify soya bean oil (SO) and sugar (CS) adulteration in milk. Bovine milk was artificially adulterated with SO (0.2–2.0%; v/v) and CS (1–10%; w/v) separately. Spectra revealed significant differences in specific wavenumber regions (SO: 1450–1250 cm^?1; CS: 1200–900 cm^?1). Soya bean oil adulteration was best predicted in wavenumber range of 1262–1164 cm^?1, using partial least square regression (coefficient of determination (R²: 0.90 and 0.88 for calibration and validation, respectively). Common sugar adulteration was best predicted in wavenumber range of 1010–910 cm^?1 (R²: 0.99 for calibration and validation) using partial least square.     

MIMO Channel Capacity and Configuration Selection for Switched Parasitic Antennas

Multiple-input multiple-output (MIMO) systems offer significant enhancements in terms of their data rate and channel capacity compared to traditional systems. However, correlation degrades the system performance and imposes practical limits on the number of antennas that can be incorporated into portable wireless devices. The use of switched parasitic antennas (SPAs) is a possible solution, especially where it is difficult to obtain sufficient signal decorrelation by conventional means. The covariance matrix represents the correlation present in the propagation channel, and has significant impact on the MIMO channel capacity. The results of this work demonstrate a significant improvement in the MIMO channel capacity by using SPA with the knowledge of the covariance matrix for all pattern configurations. By employing the “water-pouring algorithm” to modify the covariance matrix, the channel capacity is significantly improved compared to traditional systems, which spread transmit power uniformly across all the antennas. A condition number is also proposed as a selection metric to select the optimal pattern configuration for MIMO-SPAs.