A passive approach for the detection of splicing forgery in digital images

Multimedia Tools and Applications - With the technology progress, a plethora of freely accessible software has questioned the authenticity of digital images. This field is continuously creating...     

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...     

Investigation of ethanol gas sensing properties of Dy-doped SnO<Subscript>2</Subscript> nanostructures

In this paper we report doping induced enhanced sensor response of SnO₂ based sensor towards ethanol at a working temperature of 200 °C. Undoped and dysprosium-doped (Dy-doped) SnO₂ nanoparticles were characterized by X-ray diffraction (XRD), Raman spectroscopy, transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). XRD and Raman results verified tetragonal rutile structure of the prepared samples. It has been observed that crystallite size reduced with increase in dopant concentration. In addition, the particle size has been calculated from Raman spectroscopy using phonon confinement model and the values match very well with results obtained from TEM and X-ray diffraction investigations. Dy-doped SnO₂ sensors exhibited significantly enhanced response towards ethanol as compared to undoped sensor. The optimum operating temperature of doped sensor reduced to 200 °C as compared to 320 °C for that of undoped sensor. Moreover, sensor fabricated from Dy-doped SnO₂ nanostructures was highly selective toward ethanol which signifies its potential use for commercial applications. The gas sensing mechanism of SnO₂ and possible origin of enhanced sensor response has been discussed.     

Shear wave propagation in vertically heterogeneous viscoelastic layer over a micropolar elastic half-space

This article deals with the propagation of SH-wave in a vertically heterogeneous viscoelastic layer lying over a micropolar elastic half-space. Dispersion and damping equations are obtained analytically in closed form. Phase and damped velocities are computed numerically and depicted by means of a graph to exhibit the substantial effect of heterogeneity, viscoelasticity (internal friction), and micropolar parameter. As a special case of the problem, it is found that deduced dispersion relation is well in agreement to the classical-Love wave equation and damping equation vanishes identically for the isotropic case. Influence of micropolarity present in the medium of half-space is highlighted through comparative study.     

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     

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.

     

VALIDATION OF BNR (BIOLOGICAL NUTRIENT REMOVAL) PLANT

Llangefni WwTW receives discharge from the rural town of Llangefni and the local industrial estate and to date this is the first and only BNR plant in Wales. The consents from March 2003 included a reduction in ammonia (NH₄_N) to 1.5 mg/l, suspended solids to 20 mg/l (SS) and Biological Oxygen Demand (BOD) to 7 mg/l and included a new Phosphate (PO₄_P) standard of 2 mg/l. The process selected to meet the new consents was Biological Nutrient Removal (BNR) and was unusual as it was for a small, rural wastewater treatment plant that receives about 26% of its flow from an industrial estate.
During commissioning, the plant produced an average phosphate concentration of 1.0 mg/l and an ammonia concentration of 0.7 mg/l. It was confirmed that to achieve consistent phosphate removal a BOD:P ratio greater than 20:1 is required along with a high VFA (Volatile Fatty Acids) concentration of 200–300 mg/l.     

Human platelets contain a novel 47 kDa thiol-oxidase having affinity for genomic sterol-regulatory sequence

The present study provides evidence to support that human platelets possess a 47 kDa dual functional molecule having thiol-oxidase activity as well as high affinity for the SRE sequence in the human genome. On the basis of these as well as earlier results, we propose that Receptor 'Ck' dependent regulation of this dual functional 47 kDa molecule may provide a mechanism for the maintenance of cellular cholesterol homeostasis. Further, this mechanism may also explain the molecular basis of cholesterol-feedback lesion observed under premalignant conditions.